[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1904":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":15,"starSnapshotCount":15,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},1904,"paper-reading","mli\u002Fpaper-reading","mli","深度学习经典、新论文逐段精读","",null,33430,2810,814,1,0,4,30,321,25,103,"Apache License 2.0",false,"main",[25,26,27],"deep-learning","paper","reading-list","2026-06-12 04:00:11","# 深度学习论文精读\n\n## 录制完成的论文\n\n| 日期 | 标题 | 封面 | 时长 | 视频（播放数） |\n| --: | -- | -- | --: | -- |\n| 1\u002F10\u002F25 | [OpenAI Sora](https:\u002F\u002Fopenai.com\u002Findex\u002Fvideo-generation-models-as-world-simulators\u002F) 上\u003Cbr \u002F>(包含Movie Gen和HunyuanVideo) | \u003Cimg src=\"imgs\u002Fsora.jpg\" width=\"200px\"\u002F> | 1:04:18 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1VdcxesEAt)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1VdcxesEAt\u002F?share_source=copy_web&vd_source=5d037e935914fc22e2e978cdccf5cdfe)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F5MGq7dSOghY?style=social)](https:\u002F\u002Fyoutu.be\u002F5MGq7dSOghY?si=lY-OsadDsTeKf-ub)  |\n| 9\u002F04\u002F24 | Llama 3.1论文精读 · 5. 模型训练过程 | \u003Cimg src=\"imgs\u002Fllama3-process.jpg\" width=\"200px\"\u002F> | 10:41| [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1c8HbeaEXi)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1c8HbeaEXi)\u003Cbr \u002F>  |\n| 8\u002F28\u002F24 | Llama 3.1论文精读 · 4. 训练infra | \u003Cimg src=\"imgs\u002Fllama3-training-infra.webp\" width=\"200px\"\u002F> | 25:04| [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1b4421f7fa)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1b4421f7fa)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F6XidEHVjS1A?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=6XidEHVjS1A)  |\n| 8\u002F13\u002F24 | Llama 3.1论文精读 · 3. 模型 | \u003Cimg src=\"imgs\u002Fllama3-model.webp\" width=\"200px\"\u002F> | 26:14| [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1Q4421Z7Tj)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Q4421Z7Tj)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FG6gF-5g1Gg4?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=G6gF-5g1Gg4)  |\n| 8\u002F05\u002F24 | [Llama 3.1论文精读 · 2. 预训练数据](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2407.21783) | \u003Cimg src=\"imgs\u002Fllama3-pretrain-data.jpg\" width=\"200px\"\u002F> | 23:37| [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1u142187S5)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1u142187S5)[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FwXFr3zIE8FM?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wXFr3zIE8FM)|\n| 7\u002F31\u002F24 | Llama 3.1论文精读 · 1. 导言 | \u003Cimg src=\"imgs\u002Fllama3-intro.jpg\" width=\"200px\"\u002F> | 18:53| [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1WM4m1y7Uh)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1WM4m1y7Uh)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F-PztagF3wQE?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=-PztagF3wQE)  |\n| 3\u002F30\u002F23 | [GPT-4](https:\u002F\u002Fopenai.com\u002Fresearch\u002Fgpt-4) | \u003Cimg src=\"imgs\u002Fgpt4.jpg\" width=\"200px\"\u002F> | 1:20:38 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1vM4y1U7b5)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1vM4y1U7b5)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FK0SZ9mdygTw?style=social)](https:\u002F\u002Fyoutu.be\u002FK0SZ9mdygTw)  |\n| 3\u002F23\u002F23 | 大模型时代下做科研的四个思路 | \u003Cimg src=\"imgs\u002Flimited-resources.jpg\" width=\"200px\"\u002F> | 1:06:29 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1oX4y1d7X6)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1oX4y1d7X6)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Fsh79Z8i15PI?style=social)](https:\u002F\u002Fyoutu.be\u002Fsh79Z8i15PI) |\n| 3\u002F10\u002F23 | [Anthropic LLM](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.05862.pdf) | \u003Cimg src=\"imgs\u002Fanthropic_lm.jpg\" width=\"200px\"\u002F> | 1:01:51 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1XY411B7nM)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1XY411B7nM)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FiqX0pgNDon0?style=social)](https:\u002F\u002Fyoutu.be\u002FiqX0pgNDon0) |\n| 1\u002F20\u002F23 | [Helm](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.09110.pdf) 全面语言模型评测 | \u003Cimg src=\"imgs\u002Fhelm.jpg\" width=\"200px\"\u002F> | 1:23:37 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1z24y1B7uX)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1z24y1B7uX)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FWgFEw9U3BXA?style=social)](https:\u002F\u002Fyoutu.be\u002FWgFEw9U3BXA) |\n| 1\u002F11\u002F23 | 多模态论文串讲·下 |  \u003Cimg src=\"imgs\u002Fmultimodal-2.jpg\" width=\"200px\"\u002F> | 1:03:29 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1fA411Z772)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1fA411Z772) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FS1le41J76lQ?style=social)](https:\u002F\u002Fyoutu.be\u002FS1le41J76lQ) |\n| 12\u002F29\u002F22 | [Instruct GPT](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.02155.pdf) | \u003Cimg src=\"imgs\u002Finstruct-gpt.jpg\" width=\"200px\"\u002F> | 1:07:10 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1hd4y187CR)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1hd4y187CR) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FzfIGAwD1jOQ?style=social)](https:\u002F\u002Fyoutu.be\u002FzfIGAwD1jOQ) |\n| 12\u002F19\u002F22 | [Neural Corpus Indexer](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.02743.pdf) 文档检索 | \u003Cimg src=\"imgs\u002Fnci.jpg\" width=\"200px\"\u002F> | 55:47 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1Se411w7Sn)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Se411w7Sn) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FQRffZMSGJyU?style=social)](https:\u002F\u002Fyoutu.be\u002FQRffZMSGJyU) |\n| 12\u002F12\u002F22 | 多模态论文串讲·上 | \u003Cimg src=\"imgs\u002Fmultimodal-1.jpg\" width=\"200px\"\u002F> | 1:12:27 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1Vd4y1v77v)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Vd4y1v77v) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F6pzBOQAXUB8?style=social)](https:\u002F\u002Fyoutu.be\u002F6pzBOQAXUB8)  |\n| 11\u002F14\u002F22 | [OpenAI Whisper](https:\u002F\u002Fcdn.openai.com\u002Fpapers\u002Fwhisper.pdf) 精读 | \u003Cimg src=\"imgs\u002Fwhisper.jpg\" width=\"200px\"\u002F> | 1:12:16 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1VG4y1t74x)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1VG4y1t74x) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F3eXCJd32UnM?style=social)](https:\u002F\u002Fyoutu.be\u002F3eXCJd32UnM) |\n| 11\u002F07\u002F22 | 在讲 OpenAI Whisper 前先做了一个剪视频小工具 | \u003Cimg src=\"imgs\u002Fautocut.jpg\" width=\"200px\"\u002F> | 23:39 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1Pe4y1t7de)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Pe4y1t7de) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FPwVlvCPDnrI?style=social)](https:\u002F\u002Fyoutu.be\u002FPwVlvCPDnrI)  |\n| 10\u002F23\u002F22 | [Chain of Thought](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.11903.pdf) 论文、代码和资源 | \u003Cimg src=\"imgs\u002Fcot.jpg\" width=\"200px\"\u002F> | 33:21 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1t8411e7Ug)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1t8411e7Ug)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FH4J59iG3t5o?style=social)](https:\u002F\u002Fyoutu.be\u002FH4J59iG3t5o) |\n| 9\u002F17\u002F22 | CLIP 改进工作串讲（下） | \u003Cimg src=\"imgs\u002Fclipx-part2.jpg\" width=\"200px\"\u002F> | 1:04:26 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1gg411U7n4)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1gg411U7n4)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FugJeBivv65s?style=social)](https:\u002F\u002Fyoutu.be\u002FugJeBivv65s) |\n| 9\u002F2\u002F22 | CLIP 改进工作串讲（上） | \u003Cimg src=\"imgs\u002Fclipx-part1.jpg\" width=\"200px\"\u002F> | 1:14:43 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1FV4y1p7Lm)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1FV4y1p7Lm)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Fx4CDhZz_Dvg?style=social)](https:\u002F\u002Fyoutu.be\u002Fx4CDhZz_Dvg) |\n| 7\u002F29\u002F22 | [ViLT](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.03334.pdf) 论文精读 | \u003Cimg src=\"imgs\u002Fvilt.jpg\" width=\"200px\"\u002F> | 1:03:26 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV14r4y1j74y)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV14r4y1j74y)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Fug8YvZOjOCE?style=social)](https:\u002F\u002Fyoutu.be\u002Fug8YvZOjOCE) |\n| 7\u002F22\u002F22 | 理由、论据和担保【[研究的艺术](https:\u002F\u002Fpress.uchicago.edu\u002Fucp\u002Fbooks\u002Fbook\u002Fchicago\u002FC\u002Fbo23521678.html)·四】 | \u003Cimg src=\"imgs\u002Fcraft_research_p4.jpg\" width=\"200px\"\u002F> | 44:14 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1SB4y1a75c)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1SB4y1a75c) |\n| 7\u002F15\u002F22 | 如何讲好故事、故事里的论点【[研究的艺术](https:\u002F\u002Fpress.uchicago.edu\u002Fucp\u002Fbooks\u002Fbook\u002Fchicago\u002FC\u002Fbo23521678.html)·三】| \u003Cimg src=\"imgs\u002Fcraft_research_p3.jpg\" width=\"200px\"\u002F> | 43:56 |[![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1WB4y1v7ST)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1WB4y1v7ST)|\n| 7\u002F8\u002F22 | [DALL·E 2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.06125.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fdalle2.jpg\" width=\"200px\"\u002F> | 1:27:54 |[![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV17r4y1u77B)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV17r4y1u77B)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FhO57mntSMl0?style=social)](https:\u002F\u002Fyoutu.be\u002FhO57mntSMl0)|\n| 7\u002F1\u002F22 | 明白问题的重要性【[研究的艺术](https:\u002F\u002Fpress.uchicago.edu\u002Fucp\u002Fbooks\u002Fbook\u002Fchicago\u002FC\u002Fbo23521678.html)·二】| \u003Cimg src=\"imgs\u002Fcraft_research_p2.jpg\" width=\"200px\"\u002F> | 1:03:40 |[![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV11S4y1v7S2)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV11S4y1v7S2\u002F)|\n| 6\u002F24\u002F22 | 跟读者建立联系【[研究的艺术](https:\u002F\u002Fpress.uchicago.edu\u002Fucp\u002Fbooks\u002Fbook\u002Fchicago\u002FC\u002Fbo23521678.html)·一】 | \u003Cimg src=\"imgs\u002Fcraft_research_p1.jpg\" width=\"200px\"\u002F> | 45:01 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1hY411T7vy)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1hY411T7vy\u002F) |\n| 6\u002F17\u002F22 | [Zero](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.02054.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fzero.jpg\" width=\"200px\"\u002F> | 52:21 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1tY411g7ZT)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1tY411g7ZT\u002F) |\n| 6\u002F10\u002F22 | [DETR](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.12872.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fdetr.jpg\" width=\"200px\"\u002F> | 54:22 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1GB4y1X72R)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1GB4y1X72R\u002F) |\n| 6\u002F3\u002F22 | [Megatron LM](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.08053.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fmegatron_lm.jpg\" width=\"200px\"\u002F> | 56:07 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1nB4y1R7Yz)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1nB4y1R7Yz\u002F) |\n| 5\u002F27\u002F22 | [GPipe](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2019\u002Ffile\u002F093f65e080a295f8076b1c5722a46aa2-Paper.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fgpipe.jpg\" width=\"200px\"\u002F> | 58:47 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1v34y1E7zu)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1v34y1E7zu\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FeXjRpS_BTbs?style=social)](https:\u002F\u002Fyoutu.be\u002FeXjRpS_BTbs)  |\n| 5\u002F5\u002F22 | [Pathways](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12533.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fpathways.jpg\" width=\"200px\"\u002F> | 1:02:13 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1xB4y1m7Xi)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1xB4y1m7Xi\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F8hS1ZtgG0wU?style=social)](https:\u002F\u002Fyoutu.be\u002F8hS1ZtgG0wU) |\n| 4\u002F28\u002F22 | [视频理解论文串讲](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.06567.pdf)（下） | \u003Cimg src=\"imgs\u002Fvideo-survey-p2.jpg\" width=\"200px\"\u002F> | 1:08:32 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV11Y411P7ep)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV11Y411P7ep\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FJ2YC0-k57NM?style=social)](https:\u002F\u002Fyoutu.be\u002FJ2YC0-k57NM) |\n| 4\u002F21\u002F22 | [参数服务器（Parameter Server）](https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fconference\u002Fosdi14\u002Fosdi14-paper-li_mu.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fps.jpg\" width=\"200px\"\u002F> | 1:37:40 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1YA4y197G8)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1YA4y197G8\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Fxt-AwUrDxQk?style=social)](https:\u002F\u002Fyoutu.be\u002Fxt-AwUrDxQk) |\n| 4\u002F14\u002F22 | [视频理解论文串讲](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.06567.pdf)（上） | \u003Cimg src=\"imgs\u002Fvideo-survey-p1.jpg\" width=\"200px\"\u002F> | 51:15 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1fL4y157yA)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1fL4y157yA\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FgK7AGO6okhc?style=social)](https:\u002F\u002Fyoutu.be\u002FgK7AGO6okhc) |\n| 3\u002F31\u002F22 | [I3D](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.07750.pdf) 论文精读 | \u003Cimg src=\"imgs\u002Fi3d.jpg\" width=\"200px\"\u002F> | 52:31 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1tY4y1p7hq)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1tY4y1p7hq\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F9lIkKiAn6uE?style=social)](https:\u002F\u002Fyoutu.be\u002F9lIkKiAn6uE) |\n| 3\u002F24\u002F22 | 斯坦福 2022 年 [AI 指数报告](https:\u002F\u002Faiindex.stanford.edu\u002Fwp-content\u002Fuploads\u002F2022\u002F03\u002F2022-AI-Index-Report_Master.pdf) 精读 | \u003Cimg src=\"imgs\u002Fai_index_22.jpg\" width=\"200px\"\u002F> | 1:19:56 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1s44y1N7eu)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1s44y1N7eu\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FK8h_xjQ6ufY?style=social)](https:\u002F\u002Fyoutu.be\u002FK8h_xjQ6ufY) |\n| 3\u002F17\u002F22 | [AlphaCode](https:\u002F\u002Fstorage.googleapis.com\u002Fdeepmind-media\u002FAlphaCode\u002Fcompetition_level_code_generation_with_alphacode.pdf) 论文精读 | \u003Cimg src=\"imgs\u002Falphacode.jpg\" width=\"200px\"\u002F> | 44:00 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1ab4y1s7rc)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1ab4y1s7rc\u002F) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Ft8Gzkca9pW4?style=social)](https:\u002F\u002Fyoutu.be\u002Ft8Gzkca9pW4) |\n| 3\u002F10\u002F22 | [OpenAI Codex](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.03374.pdf) 论文精读 | \u003Cimg src=\"imgs\u002Fcodex.jpg\" width=\"200px\"\u002F> | 47:58 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1iY41137Zi)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1iY41137Zi\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1490959755963666432)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1490959755963666432)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FoZriUGkQSNM?style=social)](https:\u002F\u002Fyoutu.be\u002FoZriUGkQSNM) |\n| 3\u002F3\u002F22 | [GPT](https:\u002F\u002Fs3-us-west-2.amazonaws.com\u002Fopenai-assets\u002Fresearch-covers\u002Flanguage-unsupervised\u002Flanguage_understanding_paper.pdf), [GPT-2](https:\u002F\u002Fd4mucfpksywv.cloudfront.net\u002Fbetter-language-models\u002Flanguage_models_are_unsupervised_multitask_learners.pdf), [GPT-3](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.14165) 精读 | \u003Cimg src=\"imgs\u002Fgpt3.jpg\" width=\"200px\"\u002F> | 1:29:58 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1AF411b7xQ)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1AF411b7xQ\u002F)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Ft70Bl3w7bxY?style=social)](https:\u002F\u002Fyoutu.be\u002Ft70Bl3w7bxY) |\n| 2\u002F24\u002F22 | [Two-Stream](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2014\u002Ffile\u002F00ec53c4682d36f5c4359f4ae7bd7ba1-Paper.pdf) 逐段精读 |  \u003Cimg src=\"imgs\u002Ftwostream.jpg\" width=\"200px\"\u002F> | 52:57 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1mq4y1x7RU)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1mq4y1x7RU\u002F)\u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FvuqwKP2iDe0?style=social)](https:\u002F\u002Fyoutu.be\u002FvuqwKP2iDe0) |\n| 2\u002F10\u002F22 | [CLIP](https:\u002F\u002Fopenai.com\u002Fblog\u002Fclip\u002F) 逐段精读 | \u003Cimg src=\"imgs\u002Fclip.jpg\" width=\"200px\"\u002F> | 1:38:25 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1SL4y1s7LQ)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1SL4y1s7LQ\u002F)\u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1475706654562299904)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1475706654562299904) \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FOZF1t_Hieq8?style=social)](https:\u002F\u002Fyoutu.be\u002FOZF1t_Hieq8) |\n| 2\u002F6\u002F22 | 你（被）吐槽过[论文不够 novel](https:\u002F\u002Fperceiving-systems.blog\u002Fen\u002Fpost\u002Fnovelty-in-science) 吗？| \u003Cimg src=\"imgs\u002Fnovelty.jpg\" width=\"200px\"\u002F> | 14:11 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1ea41127Bq)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1ea41127Bq\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1475719090198876161)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1475719090198876161) |\n| 1\u002F23\u002F22 | [AlphaFold 2](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-021-03819-2.pdf) 精读 | \u003Cimg src=\"imgs\u002Falphafold_2.jpg\" width=\"200px\"\u002F> |  1:15:28 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1oR4y1K7Xr)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1oR4y1K7Xr\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1469132410537717760)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1469132410537717760)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FOy3OCoGUr-w?style=social)](https:\u002F\u002Fyoutu.be\u002FOy3OCoGUr-w) |\n| 1\u002F18\u002F22 | 如何判断（你自己的）研究工作的价值 | \u003Cimg src=\"imgs\u002Fresearch_value.jpg\" width=\"200px\"\u002F> |  9:59 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1oL411c7Us)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1oL411c7Us\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1475716940051869696)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1475716940051869696) |\n| 1\u002F15\u002F22 | [Swin Transformer](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.14030.pdf) 精读 | \u003Cimg src=\"imgs\u002Fswin_transformer.jpg\" width=\"200px\"\u002F> | 1:00:21 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV13L4y1475U)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV13L4y1475U\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1466282983652691968)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1466282983652691968)   \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FluP3-Fs0QCo?style=social)](https:\u002F\u002Fyoutu.be\u002FluP3-Fs0QCo) |\n| 1\u002F7\u002F22 | [指导数学直觉](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-021-04086-x.pdf) | \u003Cimg src=\"imgs\u002Fmath_conj.jpg\" width=\"200px\"\u002F> | 52:51 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1YZ4y1S72j)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1YZ4y1S72j\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1464060386375299072)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1464060386375299072)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FczFGjvhtss8?style=social)](https:\u002F\u002Fyoutu.be\u002FczFGjvhtss8) |\n| 1\u002F5\u002F22 | AlphaFold 2 预告 | \u003Cimg src=\"imgs\u002Falphafold_2_preview.jpg\" width=\"200px\"\u002F> | 03:28 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1Eu411U7Te)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Eu411U7Te\u002F) |\n| 12\u002F20\u002F21 | [对比学习](#contrastive_learning)论文综述 | \u003Cimg src=\"imgs\u002Fcontrastive.jpg\" width=\"200px\"\u002F> | 1:32:01 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV19S4y1M7hm)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV19S4y1M7hm\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1460828005077164032)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1460828005077164032)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F1pvxufGRuW4?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1pvxufGRuW4) |\n| 12\u002F15\u002F21 | [MoCo](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.05722.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fmocov1.jpg\" width=\"200px\"\u002F> | 1:24:11 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1C3411s7t9)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1C3411s7t9\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1454723120678936576)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1454723120678936576)   \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002F1pvxufGRuW4?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1pvxufGRuW4) |\n| 12\u002F9\u002F21 | 如何找研究想法 1 | \u003Cimg src=\"imgs\u002Fmae_idea.jpg\" width=\"200px\"\u002F> | 5:34 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1qq4y1z7F2)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1qq4y1z7F2\u002F) |\n| 12\u002F8\u002F21 | [MAE](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.06377.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fmae.jpg\" width=\"200px\"\u002F> | 47:04 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1sq4y1q77t)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1sq4y1q77t\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1452458167968251904)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1452458167968251904)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FmYlX2dpdHHM?style=social)](https:\u002F\u002Fyoutu.be\u002FmYlX2dpdHHM) |\n| 11\u002F29\u002F21 | [ViT](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.11929.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fvit.jpg\" width=\"200px\"\u002F> | 1:11:30 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV15P4y137jb)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV15P4y137jb\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1449195245754380288)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1449195245754380288)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FFRFt3x0bO94?style=social)](https:\u002F\u002Fyoutu.be\u002FFRFt3x0bO94) |\n| 11\u002F18\u002F21 | [BERT](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.04805) 逐段精读 | \u003Cimg src=\"imgs\u002Fbert.jpg\" width=\"200px\"\u002F> | 45:49  | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1PL411M7eQ)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1PL411M7eQ\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1445340200976785408)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1445340200976785408)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FULD3uIb2MHQ?style=social)](https:\u002F\u002Fyoutu.be\u002FULD3uIb2MHQ) |\n| 11\u002F9\u002F21 | [GAN](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2014\u002Ffile\u002F5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf) 逐段精读 | \u003Cimg src=\"imgs\u002Fgan.jpg\" width=\"200px\"\u002F> | 46:16  | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1rb4y187vD)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1rb4y187vD\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1442091389241159681)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1442091389241159681)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Fg_0HtlrLiDo?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g_0HtlrLiDo) |\n| 11\u002F3\u002F21 | 零基础多图详解 [图神经网络](https:\u002F\u002Fdistill.pub\u002F2021\u002Fgnn-intro\u002F)（GNN\u002FGCN） | \u003Cimg src=\"imgs\u002Fgnn.jpg\" width=\"200px\"\u002F> | 1:06:19 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1iT4y1d7zP)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1iT4y1d7zP\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1439540657619087360)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1439540657619087360)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FsejA2PtCITw?style=social)](https:\u002F\u002Fyoutu.be\u002FsejA2PtCITw) |\n| 10\u002F27\u002F21 | [Transformer](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.03762) 逐段精读\u003Cbr> （视频中提到的文献 [^transformer]) |\u003Cimg src=\"imgs\u002Ftransformer.jpg\" width=\"200px\"\u002F> | 1:27:05 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1pu411o7BE)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1pu411o7BE\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1437034536677404672)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1437034536677404672)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FnzqlFIcCSWQ?style=social)](https:\u002F\u002Fyoutu.be\u002FnzqlFIcCSWQ) |\n| 10\u002F22\u002F21 | [ResNet](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.03385) 论文逐段精读 | \u003Cimg src=\"imgs\u002Fresnet-2.jpg\" width=\"200px\"\u002F> | 53:46 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1P3411y7nn)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1P3411y7nn\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1434795406001180672)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1434795406001180672)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FpWMnzCX4cwQ?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pWMnzCX4cwQ) |\n| 10\u002F21\u002F21 | 撑起计算机视觉半边天的 [ResNet](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.03385) | \u003Cimg src=\"imgs\u002Fresnet-1.jpg\" width=\"200px\"\u002F> | 11:50 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1Fb4y1h73E)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1Fb4y1h73E\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1434787226101751808)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1434787226101751808)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FNnSldWhSqvY?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NnSldWhSqvY) |\n| 10\u002F15\u002F21 | [AlexNet](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2012\u002Ffile\u002Fc399862d3b9d6b76c8436e924a68c45b-Paper.pdf) 论文逐段精读 | \u003Cimg src=\"imgs\u002Falexnet-2.jpg\" width=\"200px\"\u002F> | 55:21 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1hq4y157t1)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1hq4y157t1\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1432354207483871232)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1432354207483871232)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FwYmlILPsLlY?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wYmlILPsLlY) |\n| 10\u002F14\u002F21 | 9年后重读深度学习奠基作之一：[AlexNet](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2012\u002Ffile\u002Fc399862d3b9d6b76c8436e924a68c45b-Paper.pdf) | \u003Cimg src=\"imgs\u002Falexnet-1.jpg\" width=\"200px\"\u002F> | 19:59 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1ih411J7Kz)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1ih411J7Kz\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1432155856322920448)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1432155856322920448)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002FvdYH0fE6thY?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vdYH0fE6thY) |\n| 10\u002F06\u002F21 | 如何读论文 | \u003Cimg src=\"imgs\u002Fread-paper.jpg\" width=\"200px\"\u002F> | 06:39 | [![bilibili](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1H44y1t75x)](https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV1H44y1t75x\u002F) \u003Cbr \u002F>[![zhihu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=views&style=social&logo=zhihu&query=video.play_count&url=https:\u002F\u002Fwww.zhihu.com\u002Fapi\u002Fv4\u002Fzvideos\u002F1428973951632969728)](https:\u002F\u002Fwww.zhihu.com\u002Fzvideo\u002F1428973951632969728)  \u003Cbr \u002F>[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fviews\u002Ftxjl_Q4jCyQ?style=social)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=txjl_Q4jCyQ&list=PLFXJ6jwg0qW-7UM8iUTj3qKqdhbQULP5I&index=1) |\n\n[^transformer]: 1 [斯坦福100+作者的200+页综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2108.07258)，2 [对LayerNorm的新研究](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.07013.pdf)，3 [对Attention在Transformer里面作用的研究](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.03404)\n\n\n## 所有论文\n\n包括已经录制完成和之后将要介绍的论文。选取的原则是10年内深度学习里有影响力文章（必读文章），或者近期比较有意思的文章。当然这十年里重要的工作太多了，不可能一一过一遍。在选取的时候我会偏向一些之前 [直播课](https:\u002F\u002Fc.d2l.ai\u002Fzh-v2\u002F) 中没讲到过的。 欢迎大家在 [讨论区](https:\u002F\u002Fgithub.com\u002Fmli\u002Fpaper-reading\u002Fdiscussions) 里提供建（点）议（歌）。\n\n总论文数 67，录制完成数 32\n\n（这里引用采用的是 semanticscholar，是因为它提供 [API](https:\u002F\u002Fapi.semanticscholar.org\u002Fapi-docs\u002Fgraph#operation\u002Fget_graph_get_paper) 可以自动获取，不用手动更新。）\n\n### 计算机视觉 - CNN\n\n\n| 已录制 | 年份 | 名字                                                         | 简介                 | 引用 |\n| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ |\n| ✅      | 2012 | [AlexNet](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2012\u002Ffile\u002Fc399862d3b9d6b76c8436e924a68c45b-Paper.pdf) | 深度学习热潮的奠基作                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fabd1c342495432171beb7ca8fd9551ef13cbd0ff%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImageNet-classification-with-deep-convolutional-Krizhevsky-Sutskever\u002Fabd1c342495432171beb7ca8fd9551ef13cbd0ff) |\n| | 2014 | [VGG](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1409.1556.pdf) | 使用 3x3 卷积构造更深的网络                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Feb42cf88027de515750f230b23b1a057dc782108%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FVery-Deep-Convolutional-Networks-for-Large-Scale-Simonyan-Zisserman\u002Feb42cf88027de515750f230b23b1a057dc782108) |\n| | 2014 | [GoogleNet](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1409.4842.pdf) | 使用并行架构构造更深的网络                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe15cf50aa89fee8535703b9f9512fca5bfc43327%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGoing-deeper-with-convolutions-Szegedy-Liu\u002Fe15cf50aa89fee8535703b9f9512fca5bfc43327) |\n|  ✅  | 2015 |  [ResNet](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1512.03385.pdf) | 构建深层网络都要有的残差连接。               |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F2c03df8b48bf3fa39054345bafabfeff15bfd11d%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDeep-Residual-Learning-for-Image-Recognition-He-Zhang\u002F2c03df8b48bf3fa39054345bafabfeff15bfd11d)  |\n|  | 2017 | [MobileNet](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1704.04861.pdf) | 适合终端设备的小CNN                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F3647d6d0f151dc05626449ee09cc7bce55be497e%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMobileNets%3A-Efficient-Convolutional-Neural-Networks-Howard-Zhu\u002F3647d6d0f151dc05626449ee09cc7bce55be497e)  |\n| | 2019 | [EfficientNet](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.11946.pdf) | 通过架构搜索得到的CNN                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F4f2eda8077dc7a69bb2b4e0a1a086cf054adb3f9%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEfficientNet%3A-Rethinking-Model-Scaling-for-Neural-Tan-Le\u002F4f2eda8077dc7a69bb2b4e0a1a086cf054adb3f9)  |\n| | 2021 |  [Non-deep networks](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07641.pdf) | 让不深的网络也能在ImageNet刷到SOTA                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0d7f6086772079bc3e243b7b375a9ca1a517ba8b%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FNon-deep-Networks-Goyal-Bochkovskiy\u002F0d7f6086772079bc3e243b7b375a9ca1a517ba8b) |\n\n### 计算机视觉 - Transformer\n\n| 已录制 | 年份 | 名字                                                         | 简介                 | 引用 |\n| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ |\n| ✅ | 2020 | [ViT](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.11929.pdf) | Transformer杀入CV界                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7b15fa1b8d413fbe14ef7a97f651f47f5aff3903%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-Image-is-Worth-16x16-Words%3A-Transformers-for-at-Dosovitskiy-Beyer\u002F7b15fa1b8d413fbe14ef7a97f651f47f5aff3903)  |\n| ✅ | 2021 | [Swin Transformer](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.14030.pdf) | 多层次的Vision Transformer                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc8b25fab5608c3e033d34b4483ec47e68ba109b7%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSwin-Transformer%3A-Hierarchical-Vision-Transformer-Liu-Lin\u002Fc8b25fab5608c3e033d34b4483ec47e68ba109b7) |\n| | 2021 | [MLP-Mixer](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.01601.pdf) | 使用MLP替换self-attention            |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F2def61f556f9a5576ace08911496b7c7e4f970a4%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMLP-Mixer%3A-An-all-MLP-Architecture-for-Vision-Tolstikhin-Houlsby\u002F2def61f556f9a5576ace08911496b7c7e4f970a4)  |\n| ✅ | 2021 | [MAE](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.06377.pdf) | BERT的CV版             |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc1962a8cf364595ed2838a097e9aa7cd159d3118%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMasked-Autoencoders-Are-Scalable-Vision-Learners-He-Chen\u002Fc1962a8cf364595ed2838a097e9aa7cd159d3118)  |\n\n### 生成模型\n\n| 已录制 | 年份 | 名字                                              | 简介         | 引用 |\n| ------ | ---- | ------------------------------------------------- | ------------ | ------------------------------------------------------------ |\n|  ✅ | 2014 | [GAN](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2014\u002Ffile\u002F5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf) | 生成模型的开创工作                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F54e325aee6b2d476bbbb88615ac15e251c6e8214%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGenerative-Adversarial-Nets-Goodfellow-Pouget-Abadie\u002F54e325aee6b2d476bbbb88615ac15e251c6e8214)  |\n|  | 2015 | [DCGAN](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1511.06434.pdf) | 使用CNN的GAN          |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8388f1be26329fa45e5807e968a641ce170ea078%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FUnsupervised-Representation-Learning-with-Deep-Radford-Metz\u002F8388f1be26329fa45e5807e968a641ce170ea078)  |\n|  | 2016 | [pix2pix](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1611.07004.pdf) |           |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8acbe90d5b852dadea7810345451a99608ee54c7%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImage-to-Image-Translation-with-Conditional-Isola-Zhu\u002F8acbe90d5b852dadea7810345451a99608ee54c7)  |\n|  | 2016 | [SRGAN](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1609.04802.pdf) | 图片超分辨率          |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fdf0c54fe61f0ffb9f0e36a17c2038d9a1964cba3%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPhoto-Realistic-Single-Image-Super-Resolution-Using-Ledig-Theis\u002Fdf0c54fe61f0ffb9f0e36a17c2038d9a1964cba3)  |\n|  | 2017 | [WGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07875) | 训练更加容易          |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F2f85b7376769473d2bed56f855f115e23d727094%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FWasserstein-GAN-Arjovsky-Chintala\u002F2f85b7376769473d2bed56f855f115e23d727094)  |\n|  | 2017 | [CycleGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10593) |           |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc43d954cf8133e6254499f3d68e45218067e4941%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FUnpaired-Image-to-Image-Translation-Using-Networks-Zhu-Park\u002Fc43d954cf8133e6254499f3d68e45218067e4941)  |\n|  | 2018 | [StyleGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.04948) |           |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fceb2ebef0b41e31c1a21b28c2734123900c005e2%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Style-Based-Generator-Architecture-for-Generative-Karras-Laine\u002Fceb2ebef0b41e31c1a21b28c2734123900c005e2)  |\n| | 2019 | [StyleGAN2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.04958.pdf) |        |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ff3e3d1f86a534a3654d0ee263142e44f4e2c61e9%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAnalyzing-and-Improving-the-Image-Quality-of-Karras-Laine\u002Ff3e3d1f86a534a3654d0ee263142e44f4e2c61e9)  |\n| | 2020 | [DDPM](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.11239.pdf) | Diffusion Models   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F289db3be7bf77e06e75541ba93269de3d604ac72%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDenoising-Diffusion-Probabilistic-Models-Ho-Jain\u002F289db3be7bf77e06e75541ba93269de3d604ac72)  |\n| | 2021 | [Improved DDPM](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.09672.pdf) | 改进的 DDPM   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fde18baa4964804cf471d85a5a090498242d2e79f%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImproved-Denoising-Diffusion-Probabilistic-Models-Nichol-Dhariwal\u002Fde18baa4964804cf471d85a5a090498242d2e79f)  |\n| | 2021 | [Guided Diffusion Models](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.05233.pdf) | 号称超越 GAN  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F64ea8f180d0682e6c18d1eb688afdb2027c02794%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FDiffusion-Models-Beat-GANs-on-Image-Synthesis-Dhariwal-Nichol\u002F64ea8f180d0682e6c18d1eb688afdb2027c02794)  |\n| | 2021 | [StyleGAN3](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.12423.pdf) |        |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc1ff08b59f00c44f34dfdde55cd53370733a2c19%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAlias-Free-Generative-Adversarial-Networks-Karras-Aittala\u002Fc1ff08b59f00c44f34dfdde55cd53370733a2c19)  |\n|  ✅  | 2022 | [DALL.E 2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.06125.pdf) | CLIP + Diffusion models，文本生成图像新高度     |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc57293882b2561e1ba03017902df9fc2f289dea2%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FHierarchical-Text-Conditional-Image-Generation-with-Ramesh-Dhariwal\u002Fc57293882b2561e1ba03017902df9fc2f289dea2)  |\n|  ✅  | 2024 | [Sora](https:\u002F\u002Fopenai.com\u002Findex\u002Fvideo-generation-models-as-world-simulators\u002F) | 开启视频生成热潮     |  |\n|  ✅  | 2024 | [Movie Gen](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.13720) | 精确的文本指导视频编辑、个性化视频生成     |  |\n|  ✅  | 2025 | [HunyuanVideo](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2412.03603) | 开源视频生成框架     |  |\n\n### 计算机视觉 - Object Detection\n\n| 已录制 | 年份 | 名字                                              | 简介         | 引用 |\n| ------ | ---- | ------------------------------------------------- | ------------ | ------------------------------------------------------------ |\n|        | 2014 | [R-CNN](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1311.2524v5.pdf)    | Two-stage             |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F2f4df08d9072fc2ac181b7fced6a245315ce05c8%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F2f4df08d9072fc2ac181b7fced6a245315ce05c8)  |\n|        | 2015 | [Fast R-CNN](http:\u002F\u002Farxiv.org\u002Fabs\u002F1504.08083v2)   |                       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7ffdbc358b63378f07311e883dddacc9faeeaf4b%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F7ffdbc358b63378f07311e883dddacc9faeeaf4b)  |\n|        | 2015 | [Faster R-CNN](http:\u002F\u002Farxiv.org\u002Fabs\u002F1506.01497v3) |                       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F424561d8585ff8ebce7d5d07de8dbf7aae5e7270%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F424561d8585ff8ebce7d5d07de8dbf7aae5e7270)  |\n|        | 2016 | [SSD](http:\u002F\u002Farxiv.org\u002Fabs\u002F1512.02325v5)          | Single stage          |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F4d7a9197433acbfb24ef0e9d0f33ed1699e4a5b0%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F4d7a9197433acbfb24ef0e9d0f33ed1699e4a5b0)  |\n|        | 2016 | [YOLO](http:\u002F\u002Farxiv.org\u002Fabs\u002F1506.02640v5)         |                       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ff8e79ac0ea341056ef20f2616628b3e964764cfd%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002Ff8e79ac0ea341056ef20f2616628b3e964764cfd)  |\n|        | 2017 | [Mask R-CNN](http:\u002F\u002Farxiv.org\u002Fabs\u002F1703.06870v3)   |                       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fea99a5535388196d0d44be5b4d7dd02029a43bb2%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002Fea99a5535388196d0d44be5b4d7dd02029a43bb2)  |\n|        | 2017 | [YOLOv2](http:\u002F\u002Farxiv.org\u002Fabs\u002F1612.08242v1)       |                       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7d39d69b23424446f0400ef603b2e3e22d0309d6%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002F7d39d69b23424446f0400ef603b2e3e22d0309d6)  |\n|        | 2018 | [YOLOv3](http:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02767v1)       |                       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe4845fb1e624965d4f036d7fd32e8dcdd2408148%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002Fe4845fb1e624965d4f036d7fd32e8dcdd2408148)  |\n|        | 2019 | [CenterNet](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.07850.pdf) | Anchor free           |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F6a2e2fd1b5bb11224daef98b3fb6d029f68a73f2%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FObjects-as-Points-Zhou-Wang\u002F6a2e2fd1b5bb11224daef98b3fb6d029f68a73f2)  |\n|   ✅     | 2020 | [DETR](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.12872.pdf)      | Transformer           |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F962dc29fdc3fbdc5930a10aba114050b82fe5a3e%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEnd-to-End-Object-Detection-with-Transformers-Carion-Massa\u002F962dc29fdc3fbdc5930a10aba114050b82fe5a3e)  |\n\n\u003Ca name=\"contrastive_learning\">\u003C\u002Fa>\n\n### 计算机视觉 - 对比学习\n\n\n| 已录制 | 年份 | 名字                                                         | 简介                 | 引用 |\n| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ |\n| ✅      | 2018 | [InstDisc](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.01978.pdf) | 提出实例判别和memory bank做对比学习                  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F155b7782dbd713982a4133df3aee7adfd0b6b304%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FUnsupervised-Feature-Learning-via-Non-parametric-Wu-Xiong\u002F155b7782dbd713982a4133df3aee7adfd0b6b304)  |\n| ✅      | 2018 | [CPC](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.03748.pdf) | 对比预测编码，图像语音文本强化学习全都能做                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fb227f3e4c0dc96e5ac5426b85485a70f2175a205%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FRepresentation-Learning-with-Contrastive-Predictive-Oord-Li\u002Fb227f3e4c0dc96e5ac5426b85485a70f2175a205) |\n| ✅      | 2019 | [InvaSpread](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03436.pdf) | 一个编码器的端到端对比学习                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe4bde6fe33b6c2cf9d1647ac0b041f7d1ba29c5b%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FUnsupervised-Embedding-Learning-via-Invariant-and-Ye-Zhang\u002Fe4bde6fe33b6c2cf9d1647ac0b041f7d1ba29c5b)  |\n| ✅  | 2019 |  [CMC](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.05849.pdf) | 多视角下的对比学习               |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F97f4d09175705be4677d675fa27e55defac44800%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FContrastive-Multiview-Coding-Tian-Krishnan\u002F97f4d09175705be4677d675fa27e55defac44800)  |\n| ✅ | 2019 | [MoCov1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.05722.pdf) | 无监督训练效果也很好                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fec46830a4b275fd01d4de82bffcabe6da086128f%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMomentum-Contrast-for-Unsupervised-Visual-Learning-He-Fan\u002Fec46830a4b275fd01d4de82bffcabe6da086128f) |\n|  ✅ | 2020 |  [SimCLRv1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.05709.pdf) |  简单的对比学习 (数据增强 + MLP head + 大batch训练久)                  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F34733eaf66007516347a40ad5d9bbe1cc9dacb6b%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Simple-Framework-for-Contrastive-Learning-of-Chen-Kornblith\u002F34733eaf66007516347a40ad5d9bbe1cc9dacb6b)  |\n|  ✅ | 2020 | [MoCov2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.04297.pdf) | MoCov1 + improvements from SimCLRv1                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fa1b8a8df281bbaec148a897927a49ea47ea31515%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FImproved-Baselines-with-Momentum-Contrastive-Chen-Fan\u002Fa1b8a8df281bbaec148a897927a49ea47ea31515)  |\n|  ✅ | 2020 |  [SimCLRv2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.10029.pdf) | 大的自监督预训练模型很适合做半监督学习                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F3e7f5f4382ac6f9c4fef6197dd21abf74456acd1%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FBig-Self-Supervised-Models-are-Strong-Learners-Chen-Kornblith\u002F3e7f5f4382ac6f9c4fef6197dd21abf74456acd1)  |\n| ✅  | 2020 |  [BYOL](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.07733.pdf) | 不需要负样本的对比学习                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F38f93092ece8eee9771e61c1edaf11b1293cae1b%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FBootstrap-Your-Own-Latent%3A-A-New-Approach-to-Grill-Strub\u002F38f93092ece8eee9771e61c1edaf11b1293cae1b) |\n|  ✅ | 2020 |  [SWaV](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.09882.pdf) | 聚类对比学习                   | [![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F10161d83d29fc968c4612c9e9e2b61a2fc25842e%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FUnsupervised-Learning-of-Visual-Features-by-Cluster-Caron-Misra\u002F10161d83d29fc968c4612c9e9e2b61a2fc25842e) |\n|  ✅ | 2020 |  [SimSiam](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2011.10566.pdf) | 化繁为简的孪生表征学习                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0e23d2f14e7e56e81538f4a63e11689d8ac1eb9d%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FExploring-Simple-Siamese-Representation-Learning-Chen-He\u002F0e23d2f14e7e56e81538f4a63e11689d8ac1eb9d)  |\n| ✅ | 2021 | [MoCov3](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.02057.pdf) | 如何更稳定的自监督训练ViT                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F739ceacfafb1c4eaa17509351b647c773270b3ae%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAn-Empirical-Study-of-Training-Self-Supervised-Chen-Xie\u002F739ceacfafb1c4eaa17509351b647c773270b3ae)  |\n|  ✅ | 2021 |  [DINO](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.14294.pdf) | transformer加自监督在视觉也很香                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fad4a0938c48e61b7827869e4ac3baffd0aefab35%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FEmerging-Properties-in-Self-Supervised-Vision-Caron-Touvron\u002Fad4a0938c48e61b7827869e4ac3baffd0aefab35)  |\n\n\n### 计算机视觉 - 视频理解\n\n| 已录制 | 年份 | 名字                                                         | 简介                 | 引用 |\n| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ |\n| ✅ | 2014 |  [DeepVideo](https:\u002F\u002Fcs.stanford.edu\u002Fpeople\u002Fkarpathy\u002Fdeepvideo\u002F) | 提出sports1M数据集，用深度学习做视频理解 |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F6d4c9c923e9f145d1c01a2de2afc38ec23c44253%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLarge-Scale-Video-Classification-with-Convolutional-Karpathy-Toderici\u002F6d4c9c923e9f145d1c01a2de2afc38ec23c44253)  |\n| ✅ | 2014 |  [Two-stream](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1406.2199.pdf) | 引入光流做时序建模，神经网络首次超越手工特征 |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F67dccc9a856b60bdc4d058d83657a089b8ad4486%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTwo-Stream-Convolutional-Networks-for-Action-in-Simonyan-Zisserman\u002F67dccc9a856b60bdc4d058d83657a089b8ad4486)  |\n| ✅ | 2014 |  [C3D](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1412.0767.pdf) |  比较深的3D-CNN做视频理解 |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fd25c65d261ea0e6a458be4c50c40ffe5bc508f77%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-Spatiotemporal-Features-with-3D-Networks-Tran-Bourdev\u002Fd25c65d261ea0e6a458be4c50c40ffe5bc508f77)  |\n| ✅ | 2015 |  [Beyond-short-snippets](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.08909.pdf) | 尝试使用LSTM  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F5418b2a482720e013d487a385c26fae0f017c6a6%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FBeyond-short-snippets%3A-Deep-networks-for-video-Ng-Hausknecht\u002F5418b2a482720e013d487a385c26fae0f017c6a6)  |\n| ✅ | 2016 |  [Convolutional fusion](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1604.06573.pdf) | 做early fusion来加强时空间建模    |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F9d9aced120e530484609164c836da64548693484%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FConvolutional-Two-Stream-Network-Fusion-for-Video-Feichtenhofer-Pinz\u002F9d9aced120e530484609164c836da64548693484)  |\n| ✅ | 2016 |  [TSN](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1608.00859.pdf) | 超级有效的视频分段建模，bag of tricks in video |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fea3d7de6c0880e14455b9acb28f1bc1234321456%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FTemporal-Segment-Networks%3A-Towards-Good-Practices-Wang-Xiong\u002Fea3d7de6c0880e14455b9acb28f1bc1234321456)  |\n| ✅ | 2017 |  [I3D](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1705.07750.pdf) | 提出Kinetics数据集，膨胀2D网络到3D，开启3D-CNN时代  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fb61a3f8b80bbd44f24544dc915f52fd30bbdf485%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FQuo-Vadis%2C-Action-Recognition-A-New-Model-and-the-Carreira-Zisserman\u002Fb61a3f8b80bbd44f24544dc915f52fd30bbdf485)  |\n| ✅ | 2017 |  [R2+1D](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.11248.pdf) | 拆分3D卷积核，使3D网络容易优化  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F89c3050522a0bb9820c32dc7444e003ef0d3e2e4%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FA-Closer-Look-at-Spatiotemporal-Convolutions-for-Tran-Wang\u002F89c3050522a0bb9820c32dc7444e003ef0d3e2e4)  |\n| ✅ | 2017 |  [Non-local](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.07971.pdf) | 引入自注意力做视觉问题  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8899094797e82c5c185a0893896320ef77f60e64%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FNon-local-Neural-Networks-Wang-Girshick\u002F8899094797e82c5c185a0893896320ef77f60e64)  |\n| ✅ | 2018 |  [SlowFast](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.03982.pdf) | 快慢两支提升效率   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8b47b9c3c35b2b2a78bff7822605b3040f87d699%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FSlowFast-Networks-for-Video-Recognition-Feichtenhofer-Fan\u002F8b47b9c3c35b2b2a78bff7822605b3040f87d699)  |\n| ✅ | 2021 |  [TimeSformer](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.05095.pdf) | 视频中第一个引入transformer，开启video transformer时代 |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc143ea9e30b1f2d93a9c060253845423f9e60e1f%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FIs-Space-Time-Attention-All-You-Need-for-Video-Bertasius-Wang\u002Fc143ea9e30b1f2d93a9c060253845423f9e60e1f)  |\n\n\n### 多模态学习\n\n\n| 已录制 | 年份 | 名字                                                         | 简介                 | 引用 |\n| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ |\n| ✅ | 2021 |  [CLIP](https:\u002F\u002Fopenai.com\u002Fblog\u002Fclip\u002F) | 图片和文本之间的对比学习                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F6f870f7f02a8c59c3e23f407f3ef00dd1dcf8fc4%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLearning-Transferable-Visual-Models-From-Natural-Radford-Kim\u002F6f870f7f02a8c59c3e23f407f3ef00dd1dcf8fc4)  |\n| ✅ | 2021 |  [ViLT](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.03334.pdf) | 第一个摆脱了目标检测的视觉文本模型      |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0839722fb5369c0abaff8515bfc08299efc790a1%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FViLT%3A-Vision-and-Language-Transformer-Without-or-Kim-Son\u002F0839722fb5369c0abaff8515bfc08299efc790a1)  |\n| ✅ | 2021 |  [ViLD](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.13921.pdf) | CLIP蒸馏帮助开集目标检测      |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fcf9b8da26d9b92e75ba49616ed2a1033f59fce14%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FOpen-vocabulary-Object-Detection-via-Vision-and-Gu-Lin\u002Fcf9b8da26d9b92e75ba49616ed2a1033f59fce14)  |\n| ✅ | 2021 |  [GLIP](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.03857.pdf) | 联合目标检测和文本定位           |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F5341b412383c43f4a693ad63ec4489e3ec7688c8%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FGrounded-Language-Image-Pre-training-Li-Zhang\u002F5341b412383c43f4a693ad63ec4489e3ec7688c8)  |\n| ✅ | 2021 |  [CLIP4Clip](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08860.pdf) | 拿CLIP直接做视频文本retrieval       |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F281ad83e06d731d5d686acf07cd701576f1188c4%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FCLIP4Clip%3A-An-Empirical-Study-of-CLIP-for-End-to-Luo-Ji\u002F281ad83e06d731d5d686acf07cd701576f1188c4)  |\n| ✅ | 2021 |  [ActionCLIP](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.08472.pdf) | 用多模态对比学习有监督的做视频动作分类   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fdc05240a06326b5b1664f7e8c95c330b08cd0349%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FActionCLIP%3A-A-New-Paradigm-for-Video-Action-Wang-Xing\u002Fdc05240a06326b5b1664f7e8c95c330b08cd0349)  |\n| ✅ | 2021 |  [PointCLIP](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.02413.pdf) | 3D变2D，巧妙利用CLIP做点云  |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ff3ce9ba3fcec362b70263a7ed63d9404975496a0%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FPointCLIP%3A-Point-Cloud-Understanding-by-CLIP-Zhang-Guo\u002Ff3ce9ba3fcec362b70263a7ed63d9404975496a0)  |\n| ✅ | 2022 |  [LSeg](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.03546.pdf) | 有监督的开集分割                   |[![citation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fcc9826c222ac1e81b4b374dd9e0df130f298b1e8%3Ffields%3DcitationCount)](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FLanguage-driven-Semantic-Segmentation-Li-Weinberger\u002Fcc9826c222ac1e81b4b374dd9e0df130f298b1e8)  |\n| ✅ | 2022 |  [GroupViT](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11094.pdf) | 只用图像文本对也能无监督做分割       ","该项目提供深度学习经典及最新论文的逐段精读视频。其核心功能是通过详细解析和讲解，帮助观众深入理解复杂的学术论文内容，涵盖了从模型架构、训练过程到实际应用等多个方面。技术特点在于以视频形式呈现，结合图表和实例进行直观教学，并且提供了中英文双语资源链接。非常适合对人工智能尤其是深度学习领域感兴趣的研究者、学生以及工程师用来提升专业知识水平或作为研究参考。",2,"2026-06-11 02:46:46","top_all"]