Self-Supervised Adversarial Training via Diverse Augmented Queries and Self-Supervised Double Perturbation

Our work can be seamlessly combined with models pretrained by different SSL frameworks without revising the learning objectives and helps to bridge the gap between SA T and A T. Our method also improves both robust