454 citations · 2.5k across the 90 of their papers we have counts for
9 papers · 1 filter
Learning with Multiple Complementary Labels
Lei Feng, Takuo Kaneko, Bo Han +3
A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortuna…
Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Jingfeng Zhang, Bo Han, Gang Niu +2
Deep neural networks (DNNs) are incredibly brittle due to adversarial examples. To robustify DNNs, adversarial training was proposed, which requires large-scale but well-labeled da…
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Quanming Yao, Hansi Yang, Bo Han +2
Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memo…
VILD: Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan +1
The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations. However, the quality of demonstrations in reality can be diverse, since it is easier…
Are Anchor Points Really Indispensable in Label-Noise Learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang +4
In label-noise learning, \textit{noise transition matrix}, denoting the probabilities that clean labels flip into noisy labels, plays a central role in building \textit{statistical…
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Feng Liu, Jie Lu, Bo Han +3
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However,…