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ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing (2303.17096v1)

Published 30 Mar 2023 in cs.CV

Abstract: Recent studies have shown that higher accuracy on ImageNet usually leads to better robustness against different corruptions. Therefore, in this paper, instead of following the traditional research paradigm that investigates new out-of-distribution corruptions or perturbations deep models may encounter, we conduct model debugging in in-distribution data to explore which object attributes a model may be sensitive to. To achieve this goal, we create a toolkit for object editing with controls of backgrounds, sizes, positions, and directions, and create a rigorous benchmark named ImageNet-E(diting) for evaluating the image classifier robustness in terms of object attributes. With our ImageNet-E, we evaluate the performance of current deep learning models, including both convolutional neural networks and vision transformers. We find that most models are quite sensitive to attribute changes. A small change in the background can lead to an average of 9.23\% drop on top-1 accuracy. We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models. Based on these findings, we discover ways to enhance attribute robustness with preprocessing, architecture designs, and training strategies. We hope this work can provide some insights to the community and open up a new avenue for research in robust computer vision. The code and dataset are available at https://github.com/alibaba/easyrobust.

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Authors (6)
  1. Xiaodan Li (28 papers)
  2. Yuefeng Chen (44 papers)
  3. Yao Zhu (49 papers)
  4. Shuhui Wang (54 papers)
  5. Rong Zhang (133 papers)
  6. Hui Xue (109 papers)
Citations (22)
View on Semantic Scholar

Summary

Overview of "ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing"

The paper "ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing" proposes a novel benchmark and approach for evaluating the robustness of neural networks against changes in object attributes. The authors focus on understanding how deep learning models, such as convolutional neural networks (CNNs) and vision transformers, react to variations in object attributes, including backgrounds, sizes, positions, and directions, within in-distribution data, providing a complementary approach to traditional out-of-distribution robustness studies.

Key Contributions

  1. ImageNet-E Dataset: The authors introduce the ImageNet-E dataset, specifically designed to benchmark the robustness of image classifiers by systematically altering object attributes. The dataset uses a toolkit developed for controlled editing of object attributes, ensuring the variations remain within the same distribution as the ImageNet training data.
  2. Robustness Evaluation: Extensive evaluations of contemporary deep learning models are conducted, revealing significant sensitivity to changes in attributes. For instance, a modification in background complexity leads to an average top-1 accuracy drop of 9.23%. The evaluation spans various architectures, including vanilla models, adversarially trained models, and other robustly trained networks, uncovering a stark contrast between in-distribution attribute robustness and out-of-distribution robustness.
  3. Attribute Editing Toolkit: The paper describes a novel image editing toolkit based on denoising diffusion probabilistic models (DDPMs), allowing for precise control over object attributes without leaving the original data distribution. The method effectively maintains image semantics while altering attributes, offering a new avenue for model debugging and assessment.
  4. Robustness Improvement Methods: Based on findings, the authors propose strategies to improve robustness against attribute alterations, which include preprocessing techniques and modifications to model architecture and training protocols. They further showcase that different robust training strategies, such as augmented data using DeepAugment, Augmix, or self-supervised learning via masked image modeling (e.g., MAE), can yield varying degrees of success.

Strong Numerical Results and Claims

  • The analysis shows a consistent degradation of accuracy across various edited attributes, pointing to a deficiency in models' intrinsic robustness to in-distribution variations. This suggests an immediate need for improvement in these models to ensure reliability in real-world applications.
  • The paper finds that adversarial training, while effective against conventional adversarial attacks, does not necessarily confer robustness against attribute changes. This insight could steer future directions in robust training methodologies.

Practical and Theoretical Implications

The research emphasizes the importance of testing and improving neural networks' robustness against subtle in-distribution variations that reflect real-world scenarios more closely than synthetic corruptions. This proposition could influence the design and training protocols in AI applications where stability under attribute variation is critical, such as autonomous driving and medical image analysis. Furthermore, the development of ImageNet-E could encourage more refined analysis tools and datasets focused on understanding model behavior at a more granular level, potentially leading to more generalizable and reliable AI models.

Speculation on Future Developments

Future work could leverage the insights from ImageNet-E to design new architectures and training routines that inherently account for attribute variations. This might involve developing hybrid models that combine statistical and learning-based approaches to balance both generalization across distributional shifts and resilience against specific attribute changes. Additionally, extending this research to include multi-modal contexts or more diverse datasets could prove beneficial in building universally robust AI systems.

In summary, the paper lays the groundwork for an essential paradigm shift in assessing the robustness of neural networks, offering a valuable resource for researchers aiming to fortify the dependability of machine learning systems in practical applications.