An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)
7,951#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://openreview.net/forum?id=YicbFdNTTy
Arxiv version: https://arxiv.org/abs/2010.11929
BiT Paper: https://arxiv.org/pdf/1912.11370.pdf
ImageNet-ReaL Paper: https://arxiv.org/abs/2006.07159
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
Errata:
- Patches are not flattened, but vectorized
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