Adversarial Weight Perturbation

Adversarial weight perturbation (AWP) is a technique used to improve the robustness and generalization of deep neural networks by optimizing models under adversarial perturbations of their weights, rather than input data. Current research focuses on refining AWP methods, including exploring variations like multiplicative perturbations and layer-aware approaches, often applied to models such as ResNets and Vision Transformers, to mitigate issues like catastrophic overfitting and improve efficiency. These advancements aim to enhance model performance across various tasks, from image classification to natural language processing and graph neural networks, leading to more reliable and generalizable AI systems.