Unraveling the Mysteries of Vision Transformers (ViTs): Exploring Properties, Insights, and Robustness of Their Representations

Vision Transformers (ViTs) have revolutionized the field of computer vision by demonstrating superior performance in image recognition tasks compared to traditional convolutional neural networks (CNNs) like ResNets. But what factors contribute to ViTs’ impressive performance? To answer this question, we need to delve into the learned representations of pretrained models.

One key factor that sets ViTs apart from CNNs is their ability to attend to all image patches simultaneously, allowing them to capture long-range correlations effectively. This is crucial for image classification, as it enables ViTs to learn more global and context-aware features compared to CNNs. Additionally, ViTs have been shown to be less biased towards local textures, which can limit generalization in challenging datasets.

Recent studies have delved into the robustness of ViTs compared to CNNs, revealing intriguing properties of ViTs. For example, ViTs are highly robust to occlusions, permutations, and distribution shifts, indicating their ability to learn representations that are invariant to such perturbations. ViTs also exhibit smoother loss landscapes to input perturbations, which may contribute to their robustness against adversarial attacks.

Moreover, ViTs trained with shape-based distillation or self-supervised learning have been shown to encode shape-based representations, leading to accurate semantic segmentation without pixel-level supervision. This highlights the versatility and flexibility of ViTs in learning meaningful visual representations.

Overall, the findings of these studies suggest that ViTs offer a compelling alternative to CNNs for image recognition tasks. Their ability to capture long-range correlations, learn global features, and exhibit robustness to various perturbations make them a promising choice for a wide range of computer vision applications. As the field of deep learning continues to evolve, ViTs are likely to play a significant role in advancing the state-of-the-art in image recognition and other visual tasks.