4 citations · 16 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
Cheng Cui, Zhi Ye, Yangxi Li +7
Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adop…