Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness (2010.08001v2)

Abstract: Adversarial data augmentation has shown promise for training robust deep neural networks against unforeseen data shifts or corruptions. However, it is difficult to define heuristics to generate effective fictitious target distributions containing "hard" adversarial perturbations that are largely different from the source distribution. In this paper, we propose a novel and effective regularization term for adversarial data augmentation. We theoretically derive it from the information bottleneck principle, which results in a maximum-entropy formulation. Intuitively, this regularization term encourages perturbing the underlying source distribution to enlarge predictive uncertainty of the current model, so that the generated "hard" adversarial perturbations can improve the model robustness during training. Experimental results on three standard benchmarks demonstrate that our method consistently outperforms the existing state of the art by a statistically significant margin.

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness

The paper "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" introduces an innovative approach to adversarial data augmentation (ADA) by applying a theoretical framework based on the Information Bottleneck (IB) principle. The authors propose a maximum-entropy regularization technique designed to generate more challenging adversarial perturbations, thereby enhancing the robustness and generalization of neural networks trained with ADA methods.

Theoretical Foundation

Adversarial data augmentation traditionally involves generating synthetic target distributions that include adversarial noise to simulate unanticipated data shifts and improve the model's robustness. However, defining effective perturbations that significantly diverge from the source distribution remains a challenge. This paper addresses this limitation by deriving a novel regularization term from the IB principle, resulting in a formulation that maximizes entropy in data augmentation. The intent behind this is to encourage perturbations that increase predictive uncertainty, which theoretically induces a model that is more robust against severe domain shifts.

Key Contributions and Results

1. Information Bottleneck Approach: The main contribution of this paper is the application of the IB principle to ADA. The authors establish a connection between adversarial data augmentation and information theory by focusing on the mutual information between inputs and latent representations. This is operationalized through maximum-entropy regularization, effectively controlling the distribution shift during training.
2. Empirical Performance: The method proposed is empirically validated across three major benchmarks: MNIST with domain shifts, PACS for domain generalization, and CIFAR-10/100-C for robustness against common corruptions. The results indicate consistent outperformance against state-of-the-art ADA methods. Notably, their approach achieved statistically significant improvements, specifically in scenarios demonstrating extensive domain shifts such as the MNIST-M and sketch domains in PACS.
3. Efficient Implementation: The proposed maximum-entropy regularizer is computationally efficient, requiring only a minor addition to existing ADA techniques. This makes it a pragmatic choice for practitioners looking to enhance model robustness without incurring significant additional computational costs.

Implications for Future Research and Practice

The research presented holds considerable implications for both theoretical inquiry and practical deployment in the context of robust machine learning. The incorporation of information-theoretic principles offers a promising direction for enhancing adversarial robustness. Furthermore, the simplicity of the proposed regularizer in terms of implementation suggests its potential for widespread application across various domains. The methodology could be extended to other data augmentation techniques and explored in conjunction with diverse model architectures and data types.

From a future development perspective, the paper suggests exploring alternative informational measures that might better suit regression tasks. Additionally, further investigations into augmenting non-deterministic networks and stochastic models, possibly via Bayesian techniques, could yield insights into uncertainty quantification and model reliability, thereby broadening the utility of ADA methods.

Overall, this paper provides a substantive advancement in the field of adversarial training and data augmentation strategies, marking a step toward more robust and generalizable AI systems.