Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus

• URL: http://arxiv.org/abs/2607.18561v1
• Date: Mon, 20 Jul 2026 22:48:39 GMT
• Title: Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus
• Abstract summary: We propose a novel Global Anchor-based Label Auditing method (GALA) for multi-view classification.<n>GALA outperforms eight state-of-the-art methods, especially under high noise rates.
• Score: 2.896928170172366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views. Most existing multi-view classification methods rely on accurate annotations to guarantee performance. However, noisy labels are ubiquitous in practice due to imperfect annotation, and the refinement signals that existing methods derive from models trained on such noisy supervision can gradually lose their reliability. To deal with this problem, we propose a novel Global Anchor-based Label Auditing method (GALA) for multi-view classification to resist the negative impact of noisy labels. Specifically, we construct a global anchor for each class in every view, which aggregates the samples of the whole class and thus offers a stable reference insensitive to individual predictions. Then, each view measures how close an instance is to the anchor of its observed label relative to the nearest competing anchor, and the per-view evaluations are fused with the classifier confidence into a cross-view audit score. Based on the audit scores, suspicious samples are assigned small weights, and an adaptive correction strategy rewrites a label only when the anchor-based candidate agrees with the classifier prediction. Finally, the corrected labels in turn refine the anchors and supervise noise-robust representation learning. Extensive experiments on six datasets demonstrate that GALA outperforms eight state-of-the-art methods, especially under high noise rates.

Related papers

• Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels [13.796089124499318]
  We propose a novel AL framework to construct a robust classification model by minimizing noise levels.<n>Our approach includes an assignment model that optimally assigns query points to labelers, aiming to minimize the maximum possible noise within each cycle.<n>Our experiments demonstrate that our approach significantly improves classification performance compared to several benchmark methods.
  arXiv  Detail & Related papers  (2025-12-14T23:06:37Z)
• Rethinking Consistent Multi-Label Classification under Inexact Supervision [60.79309683889278]
  In partial multi-label learning, each instance is annotated with a candidate label set, among which only some labels are relevant.<n>In complementary multi-label learning, each instance is annotated with complementary labels indicating the classes to which the instance does not belong.
  arXiv  Detail & Related papers  (2025-10-05T08:30:32Z)
• AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning [5.0823084858349485]
  We present a novel SSL algorithm named AllMatch, which achieves improved pseudo-label accuracy and a 100% utilization ratio for the unlabeled data. The results demonstrate that AllMatch consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-22T06:59:52Z)
• Trusted Multi-view Learning under Noisy Supervision [20.668620759102115]
  We propose a method to develop a reliable multi-view learning model under the guidance of noisy labels.<n>TMNR employs evidential deep neural networks to construct view-specific opinions that capture both beliefs and uncertainty.<n>TMNR2 identifies potentially mislabeled samples through evidence-label consistency and generates pseudo-labels from neighboring information.
  arXiv  Detail & Related papers  (2024-04-18T06:47:30Z)
• Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks [9.110872603799839]
  Supervised classification heavily depends on datasets annotated by humans. In subjective tasks such as toxicity classification, these annotations often exhibit low agreement among raters. In this work, we propose textbfAnnotator Awares for Texts (AART) for subjective classification tasks.
  arXiv  Detail & Related papers  (2023-11-16T10:18:32Z)
• Neighborhood Collective Estimation for Noisy Label Identification and Correction [92.20697827784426]
  Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. We propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors.
  arXiv  Detail & Related papers  (2022-08-05T14:47:22Z)
• Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels [87.48541631675889]
  We propose a two-stage clean samples identification method. First, we employ a class-level feature clustering procedure for the early identification of clean samples. Second, for the remaining clean samples that are close to the ground truth class boundary, we propose a novel consistency-based classification method.
  arXiv  Detail & Related papers  (2022-07-29T04:54:57Z)
• Resolving label uncertainty with implicit posterior models [71.62113762278963]
  We propose a method for jointly inferring labels across a collection of data samples. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs.
  arXiv  Detail & Related papers  (2022-02-28T18:09:44Z)
• Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels [4.800187500079582]
  We propose a novel method to overcome the limitations of multi-view learning. It embeds incomplete views and weak labels into a low-dimensional subspace with adaptive weights. It adaptively learns view-wise importance for embedding to detect noisy views, and mitigates the label imbalance problem by focal loss.
  arXiv  Detail & Related papers  (2022-01-04T10:49:30Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Prototypical Classifier for Robust Class-Imbalanced Learning [64.96088324684683]
  We propose textitPrototypical, which does not require fitting additional parameters given the embedding network. Prototypical produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. We test our method on CIFAR-10LT, CIFAR-100LT and Webvision datasets, observing that Prototypical obtains substaintial improvements compared with state of the arts.
  arXiv  Detail & Related papers  (2021-10-22T01:55:01Z)
• CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data [8.276156981100364]
  Co-teaching methods have shown promising improvements for computer vision problems with noisy labels. Our model, CrowdTeacher, uses the idea that robustness in the input space model can improve the perturbation of the classifier for noisy labels. We showcase the boost in predictive power attained using CrowdTeacher for both synthetic and real datasets.
  arXiv  Detail & Related papers  (2021-03-31T15:09:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.