Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels

• URL: http://arxiv.org/abs/2603.13894v1
• Date: Sat, 14 Mar 2026 11:10:17 GMT
• Title: Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
• Abstract summary: Training deep neural networks with noisy labels remains a significant challenge.<n>We propose a self-training label correction framework using decoupled bilevel optimization.<n>Our method employs noisy posterior simulation and intermediate features to transfer ground-truth knowledge.
• Score: 29.68163073890278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean dataset, our method employs noisy posterior simulation and intermediate features to transfer ground-truth knowledge, forming a closed-loop feedback system that prevents error amplification. Theoretical guarantees underpin the stability of our approach, and extensive experiments on benchmark datasets like CIFAR and Clothing1M confirm state-of-the-art performance with reduced training time, highlighting its practical applicability for learning from noisy labels.

Related papers

• Learning Earthquake Wave Arrival Time Picking from Labels with Inaccuracies [8.266165519157926]
  Inaccurately labeled training data, or "label noise", poses a significant threat to the integrity of supervised machine learning models.<n>Here, we introduce a Label Noise-Contrastive Robust Learning (LaNCoR) approach that can effectively handle noisy labels in seismic signal processing tasks.<n>Our results indicate that LaNCoR can improve performance by up to 28.8% across performance metrics.
  arXiv  Detail & Related papers  (2026-06-13T16:15:46Z)
• Variational Rectification Inference for Learning with Noisy Labels [74.85528327499662]
  We propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions.<n>VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable.<n>By introducing the variational term in VRI, the conditional posterior is estimated accurately and avoids collapsing to a Dirac delta function.
  arXiv  Detail & Related papers  (2026-03-18T01:25:08Z)
• Learning to Clean: Reinforcement Learning for Noisy Label Correction [25.643437301724365]
  This paper introduces a novel framework that conceptualizes noisy label correction as a reinforcement learning problem.<n>The proposed approach, Reinforcement Learning for Noisy Label Correction (RLNLC), defines a comprehensive state space representing data and their associated labels.<n>The effectiveness of RLNLC is demonstrated through extensive experiments on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2025-11-25T00:32:03Z)
• Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement [3.272177633069322]
  Real-world datasets often contain noisy labels due to human error, ambiguity, or resource constraints during the annotation process.<n>We propose a novel framework that combines self-supervised learning using SimCLR with iterative pseudo-label refinement.<n>Our approach significantly outperforms several state-of-the-art methods, particularly under high noise conditions.
  arXiv  Detail & Related papers  (2024-12-06T09:56:49Z)
• One-step Noisy Label Mitigation [86.57572253460125]
  Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical. We propose One-step Anti-Noise (OSA), a model-agnostic noisy label mitigation paradigm. We empirically demonstrate the superiority of OSA, highlighting its enhanced training robustness, improved task transferability, ease of deployment, and reduced computational costs.
  arXiv  Detail & Related papers  (2024-10-02T18:42:56Z)
• ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance [53.73316938815873]
  We propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE) to learn representations with error tolerance. ERASE combines prototype pseudo-labels with propagated denoised labels and updates representations with error resilience. Our method can outperform multiple baselines with clear margins in broad noise levels and enjoy great scalability.
  arXiv  Detail & Related papers  (2023-12-13T17:59:07Z)
• Fine tuning Pre trained Models for Robustness Under Noisy Labels [34.68018860186995]
  The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. We introduce a novel algorithm called TURN, which robustly and efficiently transfers the prior knowledge of pre-trained models.
  arXiv  Detail & Related papers  (2023-10-24T20:28:59Z)
• Latent Class-Conditional Noise Model [54.56899309997246]
  We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
  arXiv  Detail & Related papers  (2023-02-19T15:24:37Z)
• Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels [44.133307197696446]
  The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. We propose a novel feature embedding-based method for deep learning with label noise, termed LabEl NoiseDilution (LEND)
  arXiv  Detail & Related papers  (2022-06-27T02:45:09Z)
• Constrained Instance and Class Reweighting for Robust Learning under Label Noise [20.30785186456126]
  We propose a principled approach for tackling label noise with the aim of assigning importance weights to individual instances and class labels. Our method works by formulating a class of constrained optimization problems that yield simple closed form updates for these importance weights. We evaluate our method on several benchmark datasets and observe considerable performance gains in the presence of label noise.
  arXiv  Detail & Related papers  (2021-11-09T21:37:53Z)
• Meta Transition Adaptation for Robust Deep Learning with Noisy Labels [61.8970957519509]
  This study proposes a new meta-transition-learning strategy for the task. Specifically, through the sound guidance of a small set of meta data with clean labels, the noise transition matrix and the classifier parameters can be mutually ameliorated. Our method can more accurately extract the transition matrix, naturally following its more robust performance than prior arts.
  arXiv  Detail & Related papers  (2020-06-10T07:27:25Z)
• Learning Not to Learn in the Presence of Noisy Labels [104.7655376309784]
  We show that a new class of loss functions called the gambler's loss provides strong robustness to label noise across various levels of corruption. We show that training with this loss function encourages the model to "abstain" from learning on the data points with noisy labels.
  arXiv  Detail & Related papers  (2020-02-16T09:12:27Z)

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.