An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)

#ai #research #transformers Transformers are Ruining Convolutions. This paper, under review at ICLR, shows that given enough data, a standard Transformer can outperform Convolutional Neural Networks in image recognition tasks, which are classically tasks where CNNs excel. In this Video, I explain the architecture of the Vision Transformer (ViT), the reason why it works better and rant about why double-bline peer review is broken. OUTLINE: 0:00 - Introduction 0:30 - Double-Blind Review is Broken 5:20 - Overview 6:55 - Transformers for Images 10:40 - Vision Transformer Architecture 16:30 - Experimental Results 18:45 - What does the Model Learn? 21:00 - Why Transformers are Ruining Everything 27:45 - Inductive Biases in Transformers 29:05 - Conclusion & Comments Paper (Under Review): https://ift.tt/3d5ytIR BiT Paper: https://ift.tt/3cO0aop ImageNet-ReaL Paper: https://ift.tt/31jTcoo My Video on BiT (Big Transfer): https://youtu.be/k1GOF2jmX7c My Video on Transformers: https://youtu.be/iDulhoQ2pro My Video on BERT: https://youtu.be/-9evrZnBorM My Video on ResNets: https://youtu.be/GWt6Fu05voI Abstract: While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer can perform very well on image classification tasks when applied directly to sequences of image patches. When pre-trained on large amounts of data and transferred to multiple recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc), Vision Transformer attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train. Authors: Anonymous / Under Review Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n