From 8f122194ff6a5a322a1901f30e477e8ab2bf828f Mon Sep 17 00:00:00 2001 From: Mu Li Date: Wed, 24 Nov 2021 09:42:14 -0800 Subject: [PATCH] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 40b03e1..797b838 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,7 @@ | 已录制 | 年份 | 名字 | 简介 | 引用 | -| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ | +| ------ | ---- | ------------------------------------------------------------ | -------------------- | -----------------------------------------------------------: | | ✅ | 2012 | [AlexNet](https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf) | 深度学习热潮的奠基作 | 73342 ([link](https://www.semanticscholar.org/paper/ImageNet-classification-with-deep-convolutional-Krizhevsky-Sutskever/abd1c342495432171beb7ca8fd9551ef13cbd0ff)) | | ✅ | 2014 | [GAN](https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf) | 生成模型的开创工作 | 26024 ([link](https://www.semanticscholar.org/paper/Generative-Adversarial-Nets-Goodfellow-Pouget-Abadie/54e325aee6b2d476bbbb88615ac15e251c6e8214)) | | | 2014 | [VGG](https://arxiv.org/pdf/1409.1556.pdf) | 使用 3x3 卷积构造更深的网络 | 55856 ([link](https://www.semanticscholar.org/paper/Very-Deep-Convolutional-Networks-for-Large-Scale-Simonyan-Zisserman/eb42cf88027de515750f230b23b1a057dc782108)) | @@ -42,7 +42,7 @@ ### 计算机视觉 - Transformer | 已录制 | 年份 | 名字 | 简介 | 引用 | -| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ | +| ------ | ---- | ------------------------------------------------------------ | -------------------- | -----------------------------------------------------------: | | | 2020 | [ViT](https://arxiv.org/pdf/2010.11929.pdf) | Transformer杀入CV界 | 1527 ([link](https://www.semanticscholar.org/paper/An-Image-is-Worth-16x16-Words%3A-Transformers-for-at-Dosovitskiy-Beyer/7b15fa1b8d413fbe14ef7a97f651f47f5aff3903)) | | | 2021 | [CLIP](https://openai.com/blog/clip/) | 图片和文本之间的对比学习 | 399 ([link](https://www.semanticscholar.org/paper/Learning-Transferable-Visual-Models-From-Natural-Radford-Kim/6f870f7f02a8c59c3e23f407f3ef00dd1dcf8fc4)) | | | 2021 | [Swin Transformer](https://arxiv.org/pdf/2103.14030.pdf) | 多层次的Vision Transformer | 384 ([link](https://www.semanticscholar.org/paper/Swin-Transformer%3A-Hierarchical-Vision-Transformer-Liu-Lin/c8b25fab5608c3e033d34b4483ec47e68ba109b7)) | @@ -54,7 +54,7 @@ ### 自然语言处理 - Transformer | 已录制 | 年份 | 名字 | 简介 | 引用 | -| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ | +| ------ | ---- | ------------------------------------------------------------ | -------------------- | -----------------------------------------------------------: | | ✅ | 2017 | [Transformer](https://arxiv.org/abs/1706.03762) | 继MLP、CNN、RNN后的第四大类架构 | 26029 ([link](https://www.semanticscholar.org/paper/Attention-is-All-you-Need-Vaswani-Shazeer/204e3073870fae3d05bcbc2f6a8e263d9b72e776)) | | | 2018 | [GPT](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) | 使用 Transformer 来做预训练 | 2752 ([link](https://www.semanticscholar.org/paper/Improving-Language-Understanding-by-Generative-Radford-Narasimhan/cd18800a0fe0b668a1cc19f2ec95b5003d0a5035)) | | ✅ | 2018 | [BERT](https://arxiv.org/abs/1810.04805) | Transformer一统NLP的开始 | 25340 ([link](https://www.semanticscholar.org/paper/BERT%3A-Pre-training-of-Deep-Bidirectional-for-Devlin-Chang/df2b0e26d0599ce3e70df8a9da02e51594e0e992)) | @@ -65,7 +65,7 @@ ### 通用技术 | 已录制 | 年份 | 名字 | 简介 | 引用 | -| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ | +| ------ | ---- | ------------------------------------------------------------ | -------------------- | -----------------------------------------------------------: | | | 2014 | [Adam](https://arxiv.org/abs/1412.6980) | 深度学习里最常用的优化算法之一 | 77401 ([link](https://www.semanticscholar.org/paper/Adam%3A-A-Method-for-Stochastic-Optimization-Kingma-Ba/a6cb366736791bcccc5c8639de5a8f9636bf87e8)) | | | 2016 | [为什么超大的模型泛化性不错](https://arxiv.org/abs/1611.03530) | | 3112 ([link](https://www.semanticscholar.org/paper/Understanding-deep-learning-requires-rethinking-Zhang-Bengio/54ddb00fa691728944fd8becea90a373d21597cf)) | | | 2017 | [为什么Momentum有效](https://distill.pub/2017/momentum/) | Distill的可视化介绍 | 116 ([link](https://www.semanticscholar.org/paper/Why-Momentum-Really-Works-Goh/3e8ccf9d3d843c9855c5d76ab66d3e775384da72)) | @@ -73,7 +73,7 @@ ### 其他领域 | 已录制 | 年份 | 名字 | 简介 | 引用 | -| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ | +| ------ | ---- | ------------------------------------------------------------ | -------------------- | -----------------------------------------------------------: | | | 2014 | [Two-stream networks](https://arxiv.org/abs/1406.2199) | 首次超越手工特征的视频分类架构 | 5093 ([link](https://www.semanticscholar.org/paper/Two-Stream-Convolutional-Networks-for-Action-in-Simonyan-Zisserman/67dccc9a856b60bdc4d058d83657a089b8ad4486)) | | | 2016 | [AlphaGo](https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf) | 强化学习出圈 | 10257 ([link](https://www.semanticscholar.org/paper/Mastering-the-game-of-Go-with-deep-neural-networks-Silver-Huang/846aedd869a00c09b40f1f1f35673cb22bc87490)) | | ✅ | 2021 | [图神经网络介绍](https://distill.pub/2021/gnn-intro/) | GNN的可视化介绍 | 4 ([link](https://www.semanticscholar.org/paper/A-Gentle-Introduction-to-Graph-Neural-Networks-S%C3%A1nchez-Lengeling-Reif/2c0e0440882a42be752268d0b64243243d752a74)) |