CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels

• URL: http://arxiv.org/abs/2507.11807v1
• Date: Wed, 16 Jul 2025 00:03:07 GMT
• Title: CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
• Authors: Ruofan Hu, Dongyu Zhang, Huayi Zhang, Elke Rundensteiner,
• Abstract summary: We tackle the challenge of meta-learning for noisy label scenarios without relying on a clean labeled dataset.<n>We design the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU)<n> Experiments on benchmark datasets with varying amounts of labels under both synthetic and real-world noise demonstrate that CLID-MU outperforms state-of-the-art methods.
• Score: 5.902281427799682
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, this approach heavily depends on the availability of a clean labeled meta-dataset, which is difficult to obtain in practice. In this work, we thus tackle the challenge of meta-learning for noisy label scenarios without relying on a clean labeled dataset. Our approach leverages the data itself while bypassing the need for labels. Building on the insight that clean samples effectively preserve the consistency of related data structures across the last hidden and the final layer, whereas noisy samples disrupt this consistency, we design the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU). CLID-MU leverages the alignment of data structures across these diverse feature spaces to evaluate model performance and use this alignment to guide training. Experiments on benchmark datasets with varying amounts of labels under both synthetic and real-world noise demonstrate that CLID-MU outperforms state-of-the-art methods. The code is released at https://github.com/ruofanhu/CLID-MU.

Related papers

• Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection [84.78475642696137]
  The existence of noisy labels in real-world data negatively impacts the performance of deep learning models.<n>We propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS)<n>SGPS constructs reliable positive pairs for noisy samples to enhance the sample utilization.
  arXiv  Detail & Related papers  (2025-01-19T14:41:55Z)
• Channel-Wise Contrastive Learning for Learning with Noisy Labels [60.46434734808148]
  We introduce channel-wise contrastive learning (CWCL) to distinguish authentic label information from noise. Unlike conventional instance-wise contrastive learning (IWCL), CWCL tends to yield more nuanced and resilient features aligned with the authentic labels. Our strategy is twofold: firstly, using CWCL to extract pertinent features to identify cleanly labeled samples, and secondly, progressively fine-tuning using these samples.
  arXiv  Detail & Related papers  (2023-08-14T06:04:50Z)
• Learning to Detect Noisy Labels Using Model-Based Features [16.681748918518075]
  We propose Selection-Enhanced Noisy label Training (SENT) SENT does not rely on meta learning while having the flexibility of being data-driven. It improves performance over strong baselines under the settings of self-training and label corruption.
  arXiv  Detail & Related papers  (2022-12-28T10:12:13Z)
• Dynamic Loss For Robust Learning [17.33444812274523]
  This work presents a novel meta-learning based dynamic loss that automatically adjusts the objective functions with the training process to robustly learn a classifier from long-tailed noisy data. Our method achieves state-of-the-art accuracy on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision.
  arXiv  Detail & Related papers  (2022-11-22T01:48:25Z)
• Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning [93.63638405586354]
  We propose a simple and effective meta-training framework, called pseudo-labeling based meta-learning (PLML)<n> Firstly, we train a classifier via common semi-supervised learning (SSL) and use it to obtain the pseudo-labels of unlabeled data.<n>We build few-shot tasks from labeled and pseudo-labeled data and design a novel finetuning method with feature smoothing and noise suppression.
  arXiv  Detail & Related papers  (2022-07-14T10:53:53Z)
• 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)
• Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile [78.1212767880785]
  meta-learner is prone to overfitting since there are only a few available samples. When handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise. We present Eigen-Reptile (ER) that updates the meta- parameters with the main direction of historical task-specific parameters.
  arXiv  Detail & Related papers  (2022-06-04T08:48:02Z)
• L2B: Learning to Bootstrap Robust Models for Combating Label Noise [52.02335367411447]
  This paper introduces a simple and effective method, named Learning to Bootstrap (L2B) It enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels, as well as between different samples through meta-learning.
  arXiv  Detail & Related papers  (2022-02-09T05:57:08Z)
• MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels [0.20305676256390928]
  Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs) We propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases.
  arXiv  Detail & Related papers  (2021-03-19T15:47:44Z)
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation [78.35515004654553]
  We present a re-design of pseudo-labeling to generate structured pseudo labels for training with unlabeled or weakly-labeled data. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
  arXiv  Detail & Related papers  (2020-10-19T17:59:30Z)
• Meta Soft Label Generation for Noisy Labels [0.0]
  We propose a Meta Soft Label Generation algorithm called MSLG. MSLG can jointly generate soft labels using meta-learning techniques. Our approach outperforms other state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-07-11T19:37:44Z)

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.