3 accepted papers
Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous research has shown that self-ensemble defense methods in adversarial training (AT) still suffer from robust overfitting, which…
As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasingly more popularity. While extensive studies have been devoted to improving the model accuracy on…
Recent research on edge-preserving image smoothing has suggested that bilateral filtering is vulnerable to maliciously perturbed filtering input. However, while most prior works analyze the adaptation of the range kernel in one-step manner, in this paper we take a more constructive view towards mult…