Foster Adaptivity and Balance in Learning with Noisy Labels

• URL: http://arxiv.org/abs/2407.02778v1
• Date: Wed, 3 Jul 2024 03:10:24 GMT
• Title: Foster Adaptivity and Balance in Learning with Noisy Labels
• Authors: Mengmeng Sheng, Zeren Sun, Tao Chen, Shuchao Pang, Yucheng Wang, Yazhou Yao,
• Abstract summary: We propose a novel approach named textbfSED to deal with label noise in a textbfSelf-adaptivtextbfE and class-balancetextbfD manner. A mean-teacher model is then employed to correct labels of noisy samples. We additionally propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples.
• Score: 26.309508654960354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised models due to its effect in hurting the generalization performance of deep neural networks. Existing methods primarily employ the sample selection paradigm and usually rely on dataset-dependent prior knowledge (\eg, a pre-defined threshold) to cope with label noise, inevitably degrading the adaptivity. Moreover, existing methods tend to neglect the class balance in selecting samples, leading to biased model performance. To this end, we propose a simple yet effective approach named \textbf{SED} to deal with label noise in a \textbf{S}elf-adaptiv\textbf{E} and class-balance\textbf{D} manner. Specifically, we first design a novel sample selection strategy to empower self-adaptivity and class balance when identifying clean and noisy data. A mean-teacher model is then employed to correct labels of noisy samples. Subsequently, we propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples. Finally, we additionally employ consistency regularization on selected clean samples to improve model generalization performance. Extensive experimental results on synthetic and real-world datasets demonstrate the effectiveness and superiority of our proposed method. The source code has been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SED.

Related papers

• Extracting Clean and Balanced Subset for Noisy Long-tailed Classification [66.47809135771698]
  We develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching. By setting a manually-specific probability measure, we can reduce the side-effects of noisy and long-tailed data simultaneously. Our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise.
  arXiv  Detail & Related papers  (2024-04-10T07:34:37Z)
• Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection [82.43311784594384]
  Real-world datasets contain not only noisy labels but also class imbalance. We propose a simple yet effective method to address noisy labels in imbalanced datasets.
  arXiv  Detail & Related papers  (2024-02-17T10:34:53Z)
• Combating Label Noise With A General Surrogate Model For Sample Selection [84.61367781175984]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• Learning with Noisy labels via Self-supervised Adversarial Noisy Masking [33.87292143223425]
  We propose a novel training approach termed adversarial noisy masking. It adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. It is tested on both synthetic and real-world noisy datasets.
  arXiv  Detail & Related papers  (2023-02-14T03:13:26Z)
• Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning [10.396596055773012]
  We propose a novel complementary learning approach to enhance test-time adaptation. In test-time adaptation tasks, information from the source domain is typically unavailable. We highlight that the risk function of complementary labels agrees with their Vanilla loss formula.
  arXiv  Detail & Related papers  (2023-01-15T03:36:33Z)
• Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling [22.62790706276081]
  Training deep neural network (DNN) with noisy labels is practically challenging. Previous efforts tend to handle part or full data in a unified denoising flow. We propose a coarse-to-fine robust learning method called CREMA to handle noisy data in a divide-and-conquer manner.
  arXiv  Detail & Related papers  (2022-08-23T02:06:38Z)
• 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)
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
  We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
  arXiv  Detail & Related papers  (2021-03-24T07:26:07Z)
• Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model [80.91927573604438]
  This paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness.
  arXiv  Detail & Related papers  (2021-01-14T05:43:51Z)

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.