#044 - Data-efficient Image Transformers (Hugo Touvron)

Today we are going to talk about the Data-efficient image Transformers paper or (DeiT) which Hugo is the primary author of. One of the recipes of success for vision models since the DL revolution began has been the availability of large training sets. CNNs have been optimized for almost a decade now, including through extensive architecture search which is prone to overfitting. Motivated by the success of transformers-based models in Natural Language Processing there has been increasing attention in applying these approaches to vision models. Hugo and his collaborators used a different training strategy and a new distillation token to get a massive increase in sample efficiency with image transformers. 00:00:00 Introduction 00:06:33 Data augmentation is all you need 00:09:53 Now the image patches are the convolutions though? 00:12:16 Where are those inductive biases hiding? 00:15:46 Distillation token 00:21:01 Why different resolutions on training 00:24:14 How data efficient can