As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

Adversarial training has been demonstrated to be one of the most effective remedies for
defending adversarial examples, yet it often suffers from the huge robustness generalization …

In recent years, there has been an explosion of research into developing more robust deep
neural networks against adversarial examples. Adversarial training appears as one of the …

We show that label noise exists in adversarial training. Such label noise is due to the
mismatch between the true label distribution of adversarial examples and the label inherited …

Throughout daily life, individuals partake in various activities such as walking, sitting, and
drinking, often in a random manner. These physical activities generally exhibit similar …

Adversarial training has been demonstrated to be one of the most effective remedies for
defending adversarial examples, yet it often suffers from the huge robustness generalization …

Learning adversarially robust models require invariant predictions to a small neighborhood
of its natural inputs, often encountering insufficient model capacity. There is research …

Existing defenses against adversarial examples such as adversarial training typically
assume that the adversary will conform to a specific or known threat model, such as $\ell_p …

Contrastive learning demonstrates remarkable generalization performance but lacks
theoretical understanding, while contrastive clustering achieves promising performance but …