Robust Multi-Source Domain Adaptation under Label Shift

• URL: http://arxiv.org/abs/2503.02506v1
• Date: Tue, 04 Mar 2025 11:17:34 GMT
• Title: Robust Multi-Source Domain Adaptation under Label Shift
• Authors: Congbin Xu, Chengde Qian, Zhaojun Wang, Changliang Zou,
• Abstract summary: Unsupervised multi-source domain adaptation aims to predict labels of unlabeled samples in the target domain by using labeled samples from source domains.<n>We investigate a domain-weighted empirical risk minimization framework for robust estimation of the target domain's class proportion.
• Score: 1.3499500088995464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the volume of data continues to expand, it becomes increasingly common for data to be aggregated from multiple sources. Leveraging multiple sources for model training typically achieves better predictive performance on test datasets. Unsupervised multi-source domain adaptation aims to predict labels of unlabeled samples in the target domain by using labeled samples from source domains. This work focuses on robust multi-source domain adaptation for multi-category classification problems against the heterogeneity of label shift and data contamination. We investigate a domain-weighted empirical risk minimization framework for robust estimation of the target domain's class proportion. Inspired by outlier detection techniques, we propose a refinement procedure within this framework. With the estimated class proportion, robust classifiers for the target domain can be constructed. Theoretically, we study the finite-sample error bounds of the domain-weighted empirical risk minimization and highlight the improvement of the refinement step. Numerical simulations and real-data applications demonstrate the superiority of the proposed method.

Related papers

• Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer [69.82229895838577]
  Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains.
  arXiv  Detail & Related papers  (2023-11-21T13:12:21Z)
• Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation [9.359714425373616]
  Empirical risk often performs poorly when the distribution of the target domain differs from those of source domains. We develop an unsupervised domain adaptation approach that leverages labeled data from multiple source domains and unlabeled data from the target domain.
  arXiv  Detail & Related papers  (2023-09-05T13:19:40Z)
• Multi-Source Soft Pseudo-Label Learning with Domain Similarity-based Weighting for Semantic Segmentation [2.127049691404299]
  This paper describes a method of domain adaptive training for semantic segmentation using multiple source datasets. We propose a soft pseudo-label generation method by integrating predicted object probabilities from multiple source models.
  arXiv  Detail & Related papers  (2023-03-02T05:20:36Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation [41.356774580308986]
  We propose a novel approach named T-SVDNet to address the task of Multi-source Domain Adaptation. High-order correlations among multiple domains and categories are fully explored so as to better bridge the domain gap. To avoid negative transfer brought by noisy source data, we propose a novel uncertainty-aware weighting strategy.
  arXiv  Detail & Related papers  (2021-07-30T06:33:05Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors [13.047289562445242]
  Domain generalization (DG) deals with the problem of domain shift where a machine learning model trained on multiple-source domains fail to generalize well on a target domain with different statistics. Multiple approaches have been proposed to solve the problem of domain generalization by learning domain invariant representations across the source domains that fail to guarantee generalization on the shifted target domain. We propose a Generative Nearest Neighbor based Discrepancy Minimization (GNNDM) method which provides a theoretical guarantee that is upper bounded by the error in the labeling process of the target.
  arXiv  Detail & Related papers  (2020-07-28T14:54:25Z)
• Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
  Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
  arXiv  Detail & Related papers  (2020-03-04T23:25:09Z)

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.