Gradual Domain Adaptation for Graph Learning

• URL: http://arxiv.org/abs/2501.17443v1
• Date: Wed, 29 Jan 2025 06:48:59 GMT
• Title: Gradual Domain Adaptation for Graph Learning
• Authors: Pui Ieng Lei, Ximing Chen, Yijun Sheng, Yanyan Liu, Jingzhi Guo, Zhiguo Gong,
• Abstract summary: We present a graph gradual domain adaptation (GGDA) framework with the construction of a compact domain sequence. Our approach starts with an efficient generation of knowledge-preserving intermediate graphs over the Fused Gromov-Wasserstein (FGW) metric. Our framework concretizes the intractable inter-domain distance $W_p(mu_t,mu_t+1)$ via implementable upper and lower bounds.
• Score: 13.143891794601162
• License:
• Abstract: Existing literature lacks a graph domain adaptation technique for handling large distribution shifts, primarily due to the difficulty in simulating an evolving path from source to target graph. To make a breakthrough, we present a graph gradual domain adaptation (GGDA) framework with the construction of a compact domain sequence that minimizes information loss in adaptations. Our approach starts with an efficient generation of knowledge-preserving intermediate graphs over the Fused Gromov-Wasserstein (FGW) metric. With the bridging data pool, GGDA domains are then constructed via a novel vertex-based domain progression, which comprises "close" vertex selections and adaptive domain advancement to enhance inter-domain information transferability. Theoretically, our framework concretizes the intractable inter-domain distance $W_p(\mu_t,\mu_{t+1})$ via implementable upper and lower bounds, enabling flexible adjustments of this metric for optimizing domain formation. Extensive experiments under various transfer scenarios validate the superior performance of our GGDA framework.

Related papers

• AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation [13.472532378889264]
  In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data. This paper proposes a graph learning perspective (AGLP) for semi-supervised domain adaptation. We apply the graph convolutional network to the instance graph which allows structural information to propagate along the weighted graph edges.
  arXiv  Detail & Related papers  (2024-11-20T09:41:41Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation [9.875170018805768]
  Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. We propose a novel auxiliary task called Guidance Training. This task facilitates the effective utilization of cross-domain mixed sampling techniques while mitigating distribution shifts from the real world. We demonstrate the efficacy of our approach by integrating it with existing methods, consistently improving performance.
  arXiv  Detail & Related papers  (2024-03-22T07:12:48Z)
• SPA: A Graph Spectral Alignment Perspective for Domain Adaptation [41.89873161315133]
  Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. We introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff.
  arXiv  Detail & Related papers  (2023-10-26T17:13:48Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples [65.55521019202557]
  Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. We propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples.
  arXiv  Detail & Related papers  (2020-08-21T08:13:25Z)
• Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol [87.76993857713217]
  We show that Domain Adaptation methods using pair-wise relationships between source and target domain data can be formulated as a Graph Embedding. Specifically, we analyse the loss functions of three existing state-of-the-art Supervised Domain Adaptation methods and demonstrate that they perform Graph Embedding.
  arXiv  Detail & Related papers  (2020-04-23T15:46:20Z)
• Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision [73.76277367528657]
  Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models. We propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together.
  arXiv  Detail & Related papers  (2020-04-16T15:24:11Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.