454 citations · 2.5k across the 90 of their papers we have counts for
15 papers · 1 filter
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…
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Zhuowei Wang, Jing Jiang, Bo Han +4
Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL…
Confusable Learning for Large-class Few-Shot Classification
Bingcong Li, Bo Han, Zhuowei Wang +2
Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., t…
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han, Quanming Yao, Tongliang Liu +4
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios. However…
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
Ruize Gao, Feng Liu, Jingfeng Zhang +4
The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of ad…
Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng, Senlin Shu, Nan Lu +5
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some…