Explore a comprehensive video explanation of the machine learning research paper "TransGAN: Two Transformers Can Make One Strong GAN." Delve into the groundbreaking approach of using transformer-based architectures for both the generator and discriminator in Generative Adversarial Networks (GANs). Learn about the innovative techniques employed, including data augmentation with DiffAug, super-resolution co-training, and localized initialization of self-attention. Discover how TransGAN achieves competitive performance with convolutional GANs on various datasets and gain insights into the future potential of transformer-based GANs in computer vision tasks.
TransGAN - Two Transformers Can Make One Strong GAN - Machine Learning Research Paper Explained
Overview
Syllabus
- Introduction & Overview
- Discriminator Architecture
- Generator Architecture
- Upsampling with PixelShuffle
- Architecture Recap
- Vanilla TransGAN Results
- Trick 1: Data Augmentation with DiffAugment
- Trick 2: Super-Resolution Co-Training
- Trick 3: Locality-Aware Initialization for Self-Attention
- Scaling Up & Experimental Results
- Recap & Conclusion
Taught by
Yannic Kilcher
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