75 citations · 269 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
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…
Provably Consistent Partial-Label Learning
Lei Feng, Jiaqi Lv, Bo Han +5
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL metho…
Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei, Lei Feng, Xiangyu Chen +1
Deep Learning with noisy labels is a practically challenging problem in weakly supervised learning. The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "…