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Universal Model Adaptation by Style Augmented Open-set Consistency

Universal Model Adaptation by Style Augmented Open-set Consistency Learning to recognize unknown target samples is of great importance for unsupervised domain adaptation (UDA). Open-set domain adaptation (OSDA) and open-partial domain adaptation (OPDA) are two typical UDA scenarios, and the latter assumes that some source-private categories exist. However, most existing approaches are devised for one UDA scenario and often have bad performance on the other. Furthermore, they also demand access to source data during adaptation, leading them highly impractical due to data privacy concerns. To address the above issues, we propose a novel universal model framework that can handle both UDA scenarios without prior knowledge of the source-target label-set relationship nor access to source data. For source training, we learn a source model with both closed-set and open-set classifiers and provide it to the target domain. For target adaptation, we propose a novel Style Augmented Open-set Consistency (SAOC) objective to minimize the impact of target domain style on model behavior. Specifically, we exploit the proposed Intra-Domain Style Augmentation (IDSA) strategy to generate style-augmented target images. Then we enforce the consistency of the open-set classifier’s prediction between the image and its corresponding style-augmented version. Extensive experiments on OSDA and OPDA scenarios demonstrate that our proposed framework exhibits comparable or superior performance to some recent source-dependent approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Universal Model Adaptation by Style Augmented Open-set Consistency

Zhao, Xin; Wang, Shengsheng
Applied Intelligence , Volume 53 (19) – Oct 1, 2023
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References (16)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-023-04731-0
Publisher site
See Article on Publisher Site

Abstract

Learning to recognize unknown target samples is of great importance for unsupervised domain adaptation (UDA). Open-set domain adaptation (OSDA) and open-partial domain adaptation (OPDA) are two typical UDA scenarios, and the latter assumes that some source-private categories exist. However, most existing approaches are devised for one UDA scenario and often have bad performance on the other. Furthermore, they also demand access to source data during adaptation, leading them highly impractical due to data privacy concerns. To address the above issues, we propose a novel universal model framework that can handle both UDA scenarios without prior knowledge of the source-target label-set relationship nor access to source data. For source training, we learn a source model with both closed-set and open-set classifiers and provide it to the target domain. For target adaptation, we propose a novel Style Augmented Open-set Consistency (SAOC) objective to minimize the impact of target domain style on model behavior. Specifically, we exploit the proposed Intra-Domain Style Augmentation (IDSA) strategy to generate style-augmented target images. Then we enforce the consistency of the open-set classifier’s prediction between the image and its corresponding style-augmented version. Extensive experiments on OSDA and OPDA scenarios demonstrate that our proposed framework exhibits comparable or superior performance to some recent source-dependent approaches.

Journal

Applied Intelligence – Springer Journals

Published: Oct 1, 2023

Keywords: Universal model adaptation; Unsupervised domain adaptation; Style augmentation; Consistency regularization

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