Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation

Conventional unsupervised domain adaptation (UDA) requires access to source data and/or source model parameters, prohibiting its practical application in terms of privacy, security, and intellectual property. Recent black-box UDA (BDA) reduces such constraints by defining a pseudo label from a singl...

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Veröffentlicht in:IEEE transactions on image processing Jg. 34; S. 3181 - 3193
Hauptverfasser: Yoo, Chaehwa, Liu, Xiaofeng, Xing, Fangxu, Woo, Jonghye, Kang, Je-Won
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation
Adaptation models
Application programming interface
Application programming interfaces
Black boxes
black-box
Closed box
Data models
label refinement
Labeling
Labels
multi-source
Network security
Predictive models
Refineries
Smoothing methods
Sports
Training
Unsupervised domain adaptation
Upper bound
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Conventional unsupervised domain adaptation (UDA) requires access to source data and/or source model parameters, prohibiting its practical application in terms of privacy, security, and intellectual property. Recent black-box UDA (BDA) reduces such constraints by defining a pseudo label from a single encapsulated source application programming interface (API) prediction, which allows for self-training of the target model. Nonetheless, existing methods have limited consideration for multi-source settings, in which multiple source domain APIs are available to generate pseudo labels. In this work, we introduce a novel training framework for multi-source BDA (MSBDA), dubbed Label Space-Induced Pseudo Label Refinement (LPR). Specifically, LPR incorporates a Pseudo label Refinery Network (PRN) that learns the relationship among source domains conditioned by the target domain only utilizing source API's prediction. The target model is adapted by our dual phases PRN. First, a warm-up phase targets to avoid failure due to noisy samples and provide an initial pseudo-label, which is followed by a label refinement phase with domain relationship exploration. We provide theoretical support for the mechanism of the LPR. Experimental results on four benchmark datasets demonstrate that MSBDA using LPR achieves competitive performance compared to state-of-the-art approaches with different DA settings.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2025.3570220