10 citations · 24 across the 8 of their papers we have counts for
4 papers · 1 filter
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization
Zhuo Huang, Muyang Li, Li Shen +4
Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies…
Regularly Truncated M-estimators for Learning with Noisy Labels
Xiaobo Xia, Pengqian Lu, Chen Gong +3
The sample selection approach is very popular in learning with noisy labels. As deep networks learn pattern first, prior methods built on sample selection share a similar training…
Exploiting Counter-Examples for Active Learning with Partial labels
Fei Zhang, Yunjie Ye, Lei Feng +6
This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle…
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization
Zhuo Huang, Miaoxi Zhu, Xiaobo Xia +6
Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common…