53 citations · 59 across the 4 of their papers we have counts for
4 papers
Deep Partial Multi-Label Learning with Graph Disambiguation
Haobo Wang, Shisong Yang, Gengyu Lyu +5
In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Rec…
Generalization Bounds for Adversarial Contrastive Learning
Xin Zou, Weiwei Liu
Deep networks are well-known to be fragile to adversarial attacks, and adversarial training is one of the most popular methods used to train a robust model. To take advantage of un…
WAT: Improve the Worst-class Robustness in Adversarial Training
Boqi Li, Weiwei Liu
Deep Neural Networks (DNN) have been shown to be vulnerable to adversarial examples. Adversarial training (AT) is a popular and effective strategy to defend against adversarial att…
Better Diffusion Models Further Improve Adversarial Training
Zekai Wang, Tianyu Pang, Chao Du +3
It has been recognized that the data generated by the denoising diffusion probabilistic model (DDPM) improves adversarial training. After two years of rapid development in diffusio…