Understanding The Robustness in Vision Transformers (2204.12451v4)

Abstract: Recent studies show that Vision Transformers(ViTs) exhibit strong robustness against various corruptions. Although this property is partly attributed to the self-attention mechanism, there is still a lack of systematic understanding. In this paper, we examine the role of self-attention in learning robust representations. Our study is motivated by the intriguing properties of the emerging visual grouping in Vision Transformers, which indicates that self-attention may promote robustness through improved mid-level representations. We further propose a family of fully attentional networks (FANs) that strengthen this capability by incorporating an attentional channel processing design. We validate the design comprehensively on various hierarchical backbones. Our model achieves a state-of-the-art 87.1% accuracy and 35.8% mCE on ImageNet-1k and ImageNet-C with 76.8M parameters. We also demonstrate state-of-the-art accuracy and robustness in two downstream tasks: semantic segmentation and object detection. Code is available at: https://github.com/NVlabs/FAN.

• The paper demonstrates that self-attention in Vision Transformers leads to emergent token clustering, enhancing mid-level feature robustness.
• The paper introduces Fully Attentional Networks (FANs) that extend self-attention to channel processing, achieving 87.1% accuracy and 35.8% mCE on ImageNet benchmarks.
• The paper interprets these findings through an information bottleneck perspective, setting new performance standards in classification, segmentation, and detection tasks.

Understanding the Robustness in Vision Transformers

The research paper examines the robustness of Vision Transformers (ViTs), specifically how their architectural features contribute to resilience against image corruptions. The investigation focuses on the role of self-attention in enhancing robustness through improved mid-level representations. The authors introduce a new family of networks, named Fully Attentional Networks (FANs), which leverage attentional mechanisms for better robustness.

Key Contributions and Findings

1. Self-Attention and Robustness: The paper explores the attributes of ViTs that lead to their robustness against corruptions. The authors find that self-attention mechanisms in ViTs naturally lead to emergent clustering of image tokens. This phenomenon is hypothesized to play a significant role in enhancing the model’s robustness by promoting better mid-level feature representations.
2. Fully Attentional Networks (FANs): The research proposes FANs, which incorporate a novel attentional channel processing design. This approach extends traditional self-attention by applying it to channel processing, leading to improved model robustness and accuracy. FAN models demonstrate a significant gain in robustness, with a notable state-of-the-art 87.1% accuracy and 35.8% mean Corruption Error (mCE) on ImageNet-1k and ImageNet-C with 76.8M parameters.
3. Information Bottleneck Perspective: The paper provides an explanatory framework through the lens of information bottleneck theory. It offers insights into how self-attention facilitates the filtering of irrelevant information, leading to more clustered and robust representations. This theoretical underpinning is presented as part of the motivation for the FAN architecture.
4. Performance on Various Tasks: The FAN models are rigorously evaluated on several tasks including image classification, semantic segmentation, and object detection. In all cases, FANs set new benchmarks in terms of both accuracy and robustness to corruptions compared to previous architectures like ConvNeXt and Swin Transformer.

Numerical Results

• FAN-S-ViT exhibited a clean and robust accuracy of 82.5% and 64.6%, respectively, showing a marked improvement over comparable models such as DeiT-S and ConvNeXt.
• In downstream tasks, FAN maintained superior performance, achieving significant gains in robustness for semantic segmentation and object detection when faced with corrupted images.

Implications and Future Directions

The introduction of fully attentional designs could lead to more resilient AI systems capable of performing reliably in less controlled and potentially adversarial environments. This is particularly beneficial for applications requiring high levels of trust and accuracy, such as autonomous vehicles and medical imaging.

Future work might explore the scalability of FANs to other domains and tasks, as well as the potential integration of these findings with adversarial robustness techniques. Further research into the theoretical limits of attention-based models in improving robustness could also provide valuable insights into the design of next-generation AI architectures.

In summary, this paper presents a compelling case for the robustness benefits derived from attentional mechanisms in Vision Transformers, offering a significant step forward in our understanding and implementation of resilient deep learning models.