Emerging Properties in Self-Supervised Vision Transformers

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Second, these features are also excellent k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study also underlines the importance of momentum encoder [26], multi-crop training [9], and the use of small patches with ViTs. We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base.

1. Introduction

Transformers [57] have recently emerged as an alternative to convolutional neural networks (convnets) for visual recognition [16], [56], [68]. Their adoption has been coupled with a training strategy inspired by natural language processing (NLP), that is, pretraining on large quantities of data and finetuning on the target dataset [15], [45]. The resulting Vision Transformers (ViT) [16] are competitive with convnets but, they have not yet delivered clear benefits over them: they are computationally more demanding, require more training data, and their features do not exhibit unique properties.