Robust Overfitting

Robust overfitting in adversarial training describes the phenomenon where deep neural networks, while achieving near-perfect accuracy on adversarially perturbed training data, fail to generalize well to unseen, similarly perturbed test data. Current research focuses on mitigating this issue through techniques like data augmentation, label refinement, and novel regularization methods applied to various architectures including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Understanding and overcoming robust overfitting is crucial for developing truly robust and reliable deep learning models, with significant implications for the security and trustworthiness of AI systems in real-world applications.