3 citations · 6 across the 5 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.CV2020★ 1 cited
Adversarial Momentum-Contrastive Pre-Training
Cong Xu, Dan Li, Min Yang
Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational…
cs.LG2020★ 2 cited
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Yufeng Wang, Dan Li, Xiang Li +1
Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform…
cs.LG2020★ 3 cited
Improve Adversarial Robustness via Weight Penalization on Classification Layer
Cong Xu, Dan Li, Min Yang
It is well-known that deep neural networks are vulnerable to adversarial attacks. Recent studies show that well-designed classification parts can lead to better robustness. However…