Explore a comprehensive analysis of the groundbreaking paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" in this 30-minute video. Delve into the revolutionary Vision Transformer (ViT) architecture that outperforms Convolutional Neural Networks in image recognition tasks. Examine the reasons behind its superior performance, and critically evaluate the double-blind peer review process. Learn about the application of Transformers to image processing, understand the ViT architecture in detail, and review experimental results. Investigate what the model learns, discuss why Transformers are disrupting traditional approaches, and explore inductive biases in Transformers. Gain valuable insights into the future of computer vision and natural language processing through this in-depth explanation of cutting-edge AI research.
An Image is Worth 16x16 Words - Transformers for Image Recognition at Scale
Overview
Syllabus
- Introduction
- Double-Blind Review is Broken
- Overview
- Transformers for Images
- Vision Transformer Architecture
- Experimental Results
- What does the Model Learn?
- Why Transformers are Ruining Everything
- Inductive Biases in Transformers
- Conclusion & Comments
Taught by
Yannic Kilcher
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