Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
This work addresses a foundational issue in machine learning with implications for model compression, generalization assessment, and adversarial attacks, offering insights for improving network stability.
The paper tackles the problem of neural network sensitivity to weight perturbations and its effects on generalization and robustness, providing the first integral study and analysis for feed-forward networks and designing a theory-driven loss function to train robust models, with empirical validation.
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks. In this paper, we provide the first integral study and analysis for feed-forward neural networks in terms of the robustness in pairwise class margin and its generalization behavior under weight perturbation. We further design a new theory-driven loss function for training generalizable and robust neural networks against weight perturbations. Empirical experiments are conducted to validate our theoretical analysis. Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations.