research-article

Imbalanced Source-free Domain Adaptation

Authors:

Zi HuangAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 3330 - 3339

https://doi.org/10.1145/3474085.3475487

Published: 17 October 2021 Publication History

Abstract

Conventional Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain only when data from both domains is simultaneously accessible, which is challenged by the recent Source-free Domain Adaptation (SFDA). However, we notice that the performance of existing SFDA methods would be dramatically degraded by intra-domain class imbalance and inter-domain label shift. Unfortunately, class-imbalance is a common phenomenon in real-world domain adaptation applications. To address this issue, we present Imbalanced Source-free Domain Adaptation (ISFDA) in this paper. Specifically, we first train a uniformed model from the source domain, and then propose secondary label correction, curriculum sampling, plus intra-class tightening and inter-class separation to overcome the joint presence of covariate shift and label shift. Extensive experiments on three imbalanced benchmarks verify that ISFDA could perform favorably against existing UDA and SFDA methods under various conditions of class-imbalance, and outperform existing SFDA methods by over 15% in terms of per-class average accuracy on a large-scale long-tailed imbalanced dataset.

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Cited By

Xu ZChai ZXu CYuan CYang H(2024)Towards Effective Collaborative Learning in Long-Tailed RecognitionIEEE Transactions on Multimedia10.1109/TMM.2023.331498026(3754-3764)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3314980
Ru JTian JXiao CLi JShen H(2024)Imbalanced Open Set Domain Adaptation via Moving-Threshold Estimation and Gradual AlignmentIEEE Transactions on Multimedia10.1109/TMM.2023.329776826(2504-2514)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3297768
Jing MLi JLu KZhu LShen H(2024)Visually Source-Free Domain Adaptation via Adversarial Style MatchingIEEE Transactions on Image Processing10.1109/TIP.2024.335353933(1032-1044)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3353539
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Index Terms

Imbalanced Source-free Domain Adaptation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning

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Information & Contributors

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Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

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Published: 17 October 2021

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Sichuan Science and Technology Program
National Natural Science Foundation of China

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MM '21

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SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Xu ZChai ZXu CYuan CYang H(2024)Towards Effective Collaborative Learning in Long-Tailed RecognitionIEEE Transactions on Multimedia10.1109/TMM.2023.331498026(3754-3764)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3314980
Ru JTian JXiao CLi JShen H(2024)Imbalanced Open Set Domain Adaptation via Moving-Threshold Estimation and Gradual AlignmentIEEE Transactions on Multimedia10.1109/TMM.2023.329776826(2504-2514)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3297768
Jing MLi JLu KZhu LShen H(2024)Visually Source-Free Domain Adaptation via Adversarial Style MatchingIEEE Transactions on Image Processing10.1109/TIP.2024.335353933(1032-1044)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3353539
Li SWang ZLuo HDing LWu D(2024)T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIsIEEE Transactions on Biomedical Engineering10.1109/TBME.2023.330328971:2(423-432)Online publication date: Feb-2024
https://doi.org/10.1109/TBME.2023.3303289
Fan CWang ZGuo DWang M(2024)Data-Free Quantization via Pseudo-label Filtering2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00534(5589-5598)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00534
Li ZLiu XZhang KLi CChen J(2024)Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularizationMeasurement Science and Technology10.1088/1361-6501/ad3fd435:7(076128)Online publication date: 26-Apr-2024
https://doi.org/10.1088/1361-6501/ad3fd4
Zhang NLu JLi KFang ZZhang G(2024)Source-Free Unsupervised Domain AdaptationNeurocomputing10.1016/j.neucom.2023.126921564:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.126921
Zhang BZhang XHuang FMiao D(2024)Cross-domain knowledge collaboration for blending-target domain adaptationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10373061:4Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103730
Shu XYan SLu ZWang XXie Y(2024)AdaTriplet-RA: Domain matching via adaptive triplet and reinforced attention for unsupervised domain adaptationSignal Processing: Image Communication10.1016/j.image.2023.117024120(117024)Online publication date: Jan-2024
https://doi.org/10.1016/j.image.2023.117024
Lü SLi ZZhang XLi J(2024)Consistency regularization-based mutual alignment for source-free domain adaptationExpert Systems with Applications10.1016/j.eswa.2023.122577241(122577)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122577
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