Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint (2401.13624v1)

Abstract: Adversarial training is a widely used method to improve the robustness of deep neural networks (DNNs) over adversarial perturbations. However, it is empirically observed that adversarial training on over-parameterized networks often suffers from the \textit{robust overfitting}: it can achieve almost zero adversarial training error while the robust generalization performance is not promising. In this paper, we provide a theoretical understanding of the question of whether overfitted DNNs in adversarial training can generalize from an approximation viewpoint. Specifically, our main results are summarized into three folds: i) For classification, we prove by construction the existence of infinitely many adversarial training classifiers on over-parameterized DNNs that obtain arbitrarily small adversarial training error (overfitting), whereas achieving good robust generalization error under certain conditions concerning the data quality, well separated, and perturbation level. ii) Linear over-parameterization (meaning that the number of parameters is only slightly larger than the sample size) is enough to ensure such existence if the target function is smooth enough. iii) For regression, our results demonstrate that there also exist infinitely many overfitted DNNs with linear over-parameterization in adversarial training that can achieve almost optimal rates of convergence for the standard generalization error. Overall, our analysis points out that robust overfitting can be avoided but the required model capacity will depend on the smoothness of the target function, while a robust generalization gap is inevitable. We hope our analysis will give a better understanding of the mathematical foundations of robustness in DNNs from an approximation view.

An Approximation View on Overfitted Deep Neural Networks in Adversarial Training

The paper entitled "Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint" addresses the complex issue of robust overfitting in deep neural networks (DNNs), especially within adversarial training contexts. The research investigates whether overfitted models—those that achieve very low training error but may exhibit poor generalization—can still generalize effectively under certain conditions. The discussion is framed around a thorough theoretical analysis with insights drawn from an approximation perspective.

Key Contributions and Findings

1. Existence of Robust Classifiers: The authors constructively prove the existence of infinitely many classifiers within over-parameterized DNNs which, despite achieving negligible adversarial training error, can deliver strong robust generalization error. This result holds under specific conditions concerning data quality, separation, and perturbation levels.
2. Linear Over-parameterization: For smooth enough target functions, only linear over-parameterization—where the number of network parameters marginally exceeds the sample size—is required to achieve both low adversarial training error and robust generalization. This is particularly advantageous compared to expectations from empirical results which often suggest needing extensively larger models.
3. Analyzing Regression Paradigms: Through their analysis, the authors demonstrate analogous results for regression tasks, showing that similar infinitely many overfitted networks exist, reaching optimal convergence rates under adversarial setups.
4. Intricacies of Robust Overfitting: The work elucidates that while robust overfitting can be mitigated, the indispensable model capacity varies with the smoothness degree of the target function. Acknowledgeably, some robust generalization gap persists.

Theoretical and Practical Implications

Theoretical Insights

• Improved Understanding of Robustness: This paper advances the theoretical understanding of robust overfitting by dissecting it through the lens of approximation theory, providing a nuanced comprehension of the conditions under which adversarial training might still generalize effectively.
• Approximation Complexity: The analysis indicates the non-linear relationship between model complexity and robust generalization, offering a refined approximation perspective for judging model requirements.
• Robust Generalization Gap: The dichotomy between robust and traditional generalization emerges clearer through the proof of inherent gaps, emphasizing the need for more sophisticated theoretical treatments.

Practical Implications

• Guidance for Practitioners: Empirical practitioners can leverage these insights to better configure model architectures and adversarial training regimes, particularly focusing on data quality and perturbation limits.
• Informing Adversarial Defense Strategies: This understanding helps refine strategies to design more resilient adversarial training algorithms that minimize robust overfitting.

Future Directions

The pathways to future advancements appear manifold. One direction could entail investigating optimization algorithms that naturally lead to the desired adversarial training minima. Moreover, extending this framework to other model architectures, such as convolutional neural networks, could yield broader applicability. Exploring more nuanced forms of data quality measurements and their interplay with model architecture presents another promising research domain.

In summary, this paper delivers substantial theoretical contributions to the field of adversarial training in DNNs by blending approximation theory with an analysis of overfitting. While addressing the robust overfitting conundrum under certain preconditions, it offers a blueprint for future research and practice, paving avenues toward overcoming robustness challenges in adversarially potent environments.