24 citations · 29 across the 2 of their papers we have counts for
4 papers
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han +4
The label noise transition matrix , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and design statistically cons…
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia, Tongliang Liu, Bo Han +6
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evi…
Multi-Class Classification from Noisy-Similarity-Labeled Data
Songhua Wu, Xiaobo Xia, Tongliang Liu +5
A similarity label indicates whether two instances belong to the same class while a class label shows the class of the instance. Without class labels, a multi-class classifier coul…
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…