75 citations · 293 across the 29 of their papers we have counts for
5 papers · 1 filter
Fine-Grained Classification with Noisy Labels
Qi Wei, Lei Feng, Haoliang Sun +3
Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-gra…
ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
Mingyu Xu, Zheng Lian, Lei Feng +2
Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, nois…
IRNet: Iterative Refinement Network for Noisy Partial Label Learning
Zheng Lian, Mingyu Xu, Lan Chen +4
Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. Its basic assumption is that the ground-truth…
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 "…
Learning Cross-domain Semantic-Visual Relationships for Transductive Zero-Shot Learning
Fengmao Lv, Jianyang Zhang, Guowu Yang +3
Zero-Shot Learning (ZSL) learns models for recognizing new classes. One of the main challenges in ZSL is the domain discrepancy caused by the category inconsistency between trainin…