Conceitos Básicos
This research introduces a novel approach to adversarial training for two-layer neural networks with polynomial and ReLU activations, leveraging convex optimization to achieve globally optimal solutions and enhance robustness against adversarial attacks.
Estatísticas
On the Wisconsin Breast Cancer dataset, for an attack size of 0.9, the robust polynomial activation network achieves 76% accuracy, while the standard model drops to 15%.
Training the robust polynomial activation network on only 1% of the CIFAR-10 dataset results in significantly better robust test accuracy than sharpness-aware minimization trained on the full dataset for most attack sizes.