Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning

• URL: http://arxiv.org/abs/2508.00716v1
• Date: Fri, 01 Aug 2025 15:32:40 GMT
• Title: Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
• Authors: Yingxu Wang, Mengzhu Wang, Zhichao Huang, Suyu Liu,
• Abstract summary: Nested Graph Pseudo-Label Refinement (NeGPR) is a novel framework tailored for graph-level domain adaptation with noisy labels.<n>NeGPR consistently outperforms state-of-the-art methods under severe label noise, achieving gains of up to 12.7% in accuracy.
• Score: 9.190820361516415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Domain Adaptation (GDA) facilitates knowledge transfer from labeled source graphs to unlabeled target graphs by learning domain-invariant representations, which is essential in applications such as molecular property prediction and social network analysis. However, most existing GDA methods rely on the assumption of clean source labels, which rarely holds in real-world scenarios where annotation noise is pervasive. This label noise severely impairs feature alignment and degrades adaptation performance under domain shifts. To address this challenge, we propose Nested Graph Pseudo-Label Refinement (NeGPR), a novel framework tailored for graph-level domain adaptation with noisy labels. NeGPR first pretrains dual branches, i.e., semantic and topology branches, by enforcing neighborhood consistency in the feature space, thereby reducing the influence of noisy supervision. To bridge domain gaps, NeGPR employs a nested refinement mechanism in which one branch selects high-confidence target samples to guide the adaptation of the other, enabling progressive cross-domain learning. Furthermore, since pseudo-labels may still contain noise and the pre-trained branches are already overfitted to the noisy labels in the source domain, NeGPR incorporates a noise-aware regularization strategy. This regularization is theoretically proven to mitigate the adverse effects of pseudo-label noise, even under the presence of source overfitting, thus enhancing the robustness of the adaptation process. Extensive experiments on benchmark datasets demonstrate that NeGPR consistently outperforms state-of-the-art methods under severe label noise, achieving gains of up to 12.7% in accuracy.

Related papers

• Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise [5.943641527857957]
  Graph Neural Networks (GNNs) have gained prominence in semi-supervised learning tasks in processing graph-structured data. In real-world scenarios, labels on nodes of graphs are inevitably noisy and sparsely labeled, significantly degrading the performance of GNNs. We propose a novel GNN-CFGD that reduces the negative impact of noisy labels via coarse- and fine-grained division, along with graph reconstruction.
  arXiv  Detail & Related papers  (2024-11-06T08:21:26Z)
• Degree-Conscious Spiking Graph for Cross-Domain Adaptation [9.785526401884155]
  Spiking Graph Networks (SGNs) have demonstrated significant potential in graph classification by emulating brain-inspired neural dynamics.<n>In this paper, we first propose the domain adaptation problem in SGNs, and introduce a novel framework named Degree-Consicious Spiking Graph for Cross-Domain Adaptation.
  arXiv  Detail & Related papers  (2024-10-09T13:45:54Z)
• Inaccurate Label Distribution Learning with Dependency Noise [52.08553913094809]
  We introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning. We show that DN-ILDL effectively addresses the ILDL problem and outperforms existing LDL methods.
  arXiv  Detail & Related papers  (2024-05-26T07:58:07Z)
• 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)
• Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation [108.11783463263328]
  This paper proposes a Generative model-based Noise-Robust Training method (GeNRT) It eliminates domain shift while mitigating label noise. Experiments on Office-Home, PACS, and Digit-Five show that our GeNRT achieves comparable performance to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-10T06:43:55Z)
• Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data [23.871860648919593]
  We develop the Denoising and Nuclear-Norm Wasserstein Adaptation Network (DNAN) DNAN employs the Nuclear-norm Wasserstein discrepancy (NWD), which can simultaneously achieve domain alignment and class distinguishment. Our comprehensive experiments demonstrate that DNAN outperforms state-of-the-art methods on standard UDA benchmarks for graph classification.
  arXiv  Detail & Related papers  (2023-01-29T05:33:07Z)
• Robust Training of Graph Neural Networks via Noise Governance [27.767913371777247]
  Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. In this paper, we consider an important yet challenging scenario where labels on nodes of graphs are not only noisy but also scarce. We propose a novel RTGNN framework that achieves better robustness by learning to explicitly govern label noise.
  arXiv  Detail & Related papers  (2022-11-12T09:25:32Z)
• Rethinking Pseudo Labels for Semi-Supervised Object Detection [84.697097472401]
  We introduce certainty-aware pseudo labels tailored for object detection. We dynamically adjust the thresholds used to generate pseudo labels and reweight loss functions for each category to alleviate the class imbalance problem. Our approach improves supervised baselines by up to 10% AP using only 1-10% labeled data from COCO.
  arXiv  Detail & Related papers  (2021-06-01T01:32:03Z)
• Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation [35.728603077621564]
  Existing Unsupervised Domain Adaptation (UDA) methods presumes source and target domain data to be simultaneously available during training. A pre-trained source model is always considered to be available, even though performing poorly on target due to the well-known domain shift problem. We propose a unified method to tackle adaptive noise filtering and pseudo-label refinement.
  arXiv  Detail & Related papers  (2021-03-29T22:18:34Z)
• Unified Robust Training for Graph NeuralNetworks against Label Noise [12.014301020294154]
  We propose a new framework, UnionNET, for learning with noisy labels on graphs under a semi-supervised setting. Our approach provides a unified solution for robustly training GNNs and performing label correction simultaneously.
  arXiv  Detail & Related papers  (2021-03-05T01:17:04Z)
• A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data [69.091485888121]
  Unsupervised domain adaptation assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. We propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels.
  arXiv  Detail & Related papers  (2020-12-10T01:42:35Z)
• Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification [56.97651712118167]
  Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. We propose an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels.
  arXiv  Detail & Related papers  (2020-01-06T12:42:58Z)

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.