75 citations · 293 across the 29 of their papers we have counts for
29 papers
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
Suqin Yuan, Lei Feng, Tongliang Liu
Sample selection is a prevalent method in learning with noisy labels, where small-loss data are typically considered as correctly labeled data. However, this method may not effecti…
Exploiting Counter-Examples for Active Learning with Partial labels
Fei Zhang, Yunjie Ye, Lei Feng +6
This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle…
Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
Shuo He, Lei Feng, Guowu Yang
Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated…
A Universal Unbiased Method for Classification from Aggregate Observations
Zixi Wei, Lei Feng, Bo Han +4
In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances,…
Weakly Supervised Regression with Interval Targets
Xin Cheng, Yuzhou Cao, Ximing Li +2
This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regress…
Partial-Label Regression
Xin Cheng, Deng-Bao Wang, Lei Feng +2
Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial…
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
Senlin Shu, Shuo He, Haobo Wang +3
In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes…
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…
CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
Shiyu Tian, Hongxin Wei, Yiqun Wang +1
Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth l…
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…
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…
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
Renchunzi Xie, Hongxin Wei, Lei Feng +1
This paper studies weakly supervised domain adaptation(WSDA) problem, where we only have access to the source domain with noisy labels, from which we need to transfer useful inform…
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Haobo Wang, Ruixuan Xiao, Yixuan Li +4
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation s…
Learning from Similarity-Confidence Data
Yuzhou Cao, Lei Feng, Yitian Xu +3
Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data. In this paper, we investigate a no…
With False Friends Like These, Who Can Notice Mistakes?
Lue Tao, Lei Feng, Jinfeng Yi +1
Adversarial examples crafted by an explicit adversary have attracted significant attention in machine learning. However, the security risk posed by a potential false friend has bee…
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…
COMET: Convolutional Dimension Interaction for Collaborative Filtering
Zhuoyi Lin, Lei Feng, Xingzhi Guo +4
Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions…
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 "…