The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher mod-eling capacity and representation flexibility, than traditional convolutional networks. However, it is questionable whether such potential has been fully unleashed in prac-tice, as the learned ViTs often suffer from over-smoothening, yielding likely redundant models. Recent works made pre-liminary attempts to identify and alleviate such redundancy, e.g., via regularizing embedding similarity or re-injecting convolution-like structures. However, a “head-to-toe as-sessment” regarding the extent of redundancy in ViTs, and how much we could gain by thoroughly mitigating such, has been absent for this field. This paper, for the first time, systematically studies the ubiquitous existence of re-dundancy at all three levels: patch embedding, attention map, and weight space. In view of them, we advocate a principle of diversity for training ViTs, by presenting cor-responding regularizers that encourage the representation diversity and coverage at each of those levels, that enabling capturing more discriminative information. Extensive ex-periments on ImageNet with a number of ViT backbones validate the effectiveness of our proposals, largely eliminating the observed ViT redundancy and significantly boosting the model generalization. For example, our diversified DeiT obtains 0.70% ~ 1.76% accuracy boosts on ImageNet with highly reduced similarity. Our codes are fully available in https://github.com/VITA-Group/Diverse-ViT.