10 citations · 24 across the 8 of their papers we have counts for
8 papers
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…
Watermarking for Out-of-distribution Detection
Qizhou Wang, Feng Liu, Yonggang Zhang +4
Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. However, existing methods largely ignore the reprogr…
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Zhuo Huang, Xiaobo Xia, Li Shen +4
Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of dif…
Understanding Robust Overfitting of Adversarial Training and Beyond
Chaojian Yu, Bo Han, Li Shen +4
Robust overfitting widely exists in adversarial training of deep networks. The exact underlying reasons for this are still not completely understood. Here, we explore the causes of…
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation
Chenhong Zhou, Feng Liu, Chen Gong +4
In semantic segmentation, we aim to train a pixel-level classifier to assign category labels to all pixels in an image, where labeled training images and unlabeled test images are…