36 citations · 49 across the 4 of their papers we have counts for
4 papers
Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization
Shuo Ye, Shujian Yu, Wenjin Hou +2
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the traini…
Adversarial Robustness under Long-Tailed Distribution
Tong Wu, Ziwei Liu, Qingqiu Huang +2
Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversaria…
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
Tong Wu, Qingqiu Huang, Ziwei Liu +2
We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions. Compared to conventional sin…
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
Xudong Sun, Alexej Gossmann, Yu Wang +1
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution s…