The proposed Soft-label Refurbishment with Ensemble Learning (SoREL), a two-stage framework that jointly handles label noise and class imbalance, and refurbished soft labels guide multi-expert ensemble learning, where experts specialize in many, medium, and few-shot classes.
This article utilizes bias-variance-diversity (BVD) decomposition theory to examine the impact of noisy labels on three mainstream ensemble paradigms: Bagging, Boosting, and Stacking and characterizes the mechanisms behind performance degradation.
Guanxiong He, Jie Wang, Zhiyong Li et al.· IEEE Transactions on Neural...· 1 citation
In crowdsourcing scenarios, where each instance is labeled with multiple noisy labels, its true label is estimated by combining label integration and various recently proposed noise correction methods. Recent correction methods typically partition the data into clean and noisy sets, then train classifiers on a high-consistency clean set to relabel the remaining noisy instances. However, under high residual noise after label integration, these methods often lead to insufficient class coverage, where some classes are severely underrepresented or even absent in the clean set, making subsequent correction unreliable and bias-prone. To address this issue, we propose a class coverage-aware noise correction method for crowdsourced labels via elimination-based inference (COVER-EL). At first, COVER-EL estimates instancewise label confidence from multiple noisy labels to construct an initial clean set. Then, one-versus-rest (OvR) binary subpredictors are trained with adaptive confidence thresholds to ensure reliable predictions. For each label-ambiguous instance, COVER-EL forms a candidate label set from the distinct crowd-provided labels and iteratively eliminates ambiguous candidates using reliable classwise subpredictors, with predictor-label refinement repeated until stabilization. Theoretically, we provide a probably approximately correct (PAC)-style bound that gives a high-probability guarantee on the overall error after correction. Experimental results on 34 simulated datasets and two real-world crowdsourced datasets show that COVER-EL outperforms existing state-of-the-art correction methods.
Bi Wang, Yan-Ting Yang, Xuelian Li· IEEE Transactions on Neural...· 0 citations
This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and representation spaces.
Yujing Liu, Yixin Liu, Yu Zheng et al.· 0 citations
While SSL outperforms training from scratch on average and remains competitive with state-of-the-art tree ensembles, the SSL-vs-scratch gains exhibit high inter-task variance and lack significance, indicating the findings reflect general properties of tabular SSL rather than idiosyncrasies of one particular pretext task.
This work conducts a comparative empirical study of five MU methods across symmetric, asymmetric, instance-dependent, and open-set noise on CIFAR-10, CIFAR-100, and the real-world noisy dataset Food-101N and finds that the appropriate unlearning strategy is conditioned on the noise structure.
Experimental results on CIFAR-10, CIFAR-100, Animal-10N, and Mini-WebVision, together with additional evaluation under open-set noise, show that the proposed CANNE method achieves competitive performance across diverse noisy-label settings.
Ge Jin, Qian Zhang, Li Huang et al.· Entropy· 0 citations
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