423 citations · 1.1k across the 44 of their papers we have counts for
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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…
Deep Polynomial Neural Networks
Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. T…
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Yu Yao, Tongliang Liu, Bo Han +4
The transition matrix, denoting the transition relationship from clean labels to noisy labels, is essential to build statistically consistent classifiers in label-noise learning. E…
nets: Deep Polynomial Neural Networks
Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. Th…