ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance

• URL: http://arxiv.org/abs/2312.08852v2
• Date: Fri, 8 Mar 2024 12:29:44 GMT
• Title: ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
• Authors: Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, and Tongliang Liu
• Abstract summary: 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.
• Score: 53.73316938815873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such datasets can be cost-prohibitive, leading to the practical use of labels obtained from economically efficient sources such as web searches and user tags. Unfortunately, these labels often come with noise, compromising the generalization performance of deep networks. To tackle this challenge and enhance the robustness of deep learning models against label noise in graph-based tasks, we propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE). The core idea of ERASE is to learn representations with error tolerance by maximizing coding rate reduction. Particularly, we introduce a decoupled label propagation method for learning representations. Before training, noisy labels are pre-corrected through structural denoising. During training, ERASE combines prototype pseudo-labels with propagated denoised labels and updates representations with error resilience, which significantly improves the generalization performance in node classification. The proposed method allows us to more effectively withstand errors caused by mislabeled nodes, thereby strengthening the robustness of deep networks in handling noisy graph data. Extensive experimental results show that our method can outperform multiple baselines with clear margins in broad noise levels and enjoy great scalability. Codes are released at https://github.com/eraseai/erase.

Related papers

• CONVERT:Contrastive Graph Clustering with Reliable Augmentation [110.46658439733106]
  We propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT) In our method, the data augmentations are processed by the proposed reversible perturb-recover network. To further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network.
  arXiv  Detail & Related papers  (2023-08-17T13:07:09Z)
• Robust Feature Learning Against Noisy Labels [0.2082426271304908]
  Mislabeled samples can significantly degrade the generalization of models. progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels.
  arXiv  Detail & Related papers  (2023-07-10T02:55:35Z)
• Learning on Graphs under Label Noise [5.909452203428086]
  We develop a novel approach dubbed Consistent Graph Neural Network (CGNN) to solve the problem of learning on graphs with label noise. Specifically, we employ graph contrastive learning as a regularization term, which promotes two views of augmented nodes to have consistent representations. To detect noisy labels on the graph, we present a sample selection technique based on the homophily assumption.
  arXiv  Detail & Related papers  (2023-06-14T01:38:01Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• 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)
• Informative Pseudo-Labeling for Graph Neural Networks with Few Labels [12.83841767562179]
  Graph Neural Networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. The challenge of how to effectively learn GNNs with very few labels is still under-explored. We propose a novel informative pseudo-labeling framework, called InfoGNN, to facilitate learning of GNNs with extremely few labels.
  arXiv  Detail & Related papers  (2022-01-20T01:49:30Z)
• Noisy Labels Can Induce Good Representations [53.47668632785373]
  We study how architecture affects learning with noisy labels. We show that training with noisy labels can induce useful hidden representations, even when the model generalizes poorly. This finding leads to a simple method to improve models trained on noisy labels.
  arXiv  Detail & Related papers  (2020-12-23T18:58:05Z)
• Learn to Propagate Reliably on Noisy Affinity Graphs [69.97364913330989]
  Recent works have shown that exploiting unlabeled data through label propagation can substantially reduce the labeling cost. How to propagate labels reliably, especially on a dataset with unknown outliers, remains an open question. We propose a new framework that allows labels to be propagated reliably on large-scale real-world data.
  arXiv  Detail & Related papers  (2020-07-17T07:55:59Z)
• Temporal Calibrated Regularization for Robust Noisy Label Learning [60.90967240168525]
  Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets. However, labeling large-scale data can be very costly and error-prone so that it is difficult to guarantee the annotation quality. We propose a Temporal Calibrated Regularization (TCR) in which we utilize the original labels and the predictions in the previous epoch together.
  arXiv  Detail & Related papers  (2020-07-01T04:48:49Z)

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.