75 citations · 193 across the 22 of their papers we have counts for
8 papers · 1 filter
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Hongxin Wei, Huiping Zhuang, Renchunzi Xie +4
In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been s…
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…
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Haobo Wang, Mingxuan Xia, Yixuan Li +4
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single grou…
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
Hongxin Wei, Lue Tao, Renchunzi Xie +2
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance. Recent studies found that directly training with out-of-distribution dat…
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
Ruixuan Xiao, Yiwen Dong, Haobo Wang +4
Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific s…
Mitigating Neural Network Overconfidence with Logit Normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng +3
Detecting out-of-distribution inputs is critical for safe deployment of machine learning models in the real world. However, neural networks are known to suffer from the overconfide…