Attention Guided Multiple Source and Target Domain Adaptation

Domain adaptation aims to alleviate the distribution discrepancy between source and target domains. Most conventional methods focus on one target domain setting adapted from one or multiple source domains while neglecting the multi-target domain setting. We argue that different target domains also h...

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Bibliographic Details
Published in:IEEE transactions on image processing 2021, Vol.30, p.892-906
Main Authors: Wang, Yuxi, Zhang, Zhaoxiang, Hao, Wangli, Song, Chunfeng
Format: Article
Language:English
Subjects:
Adaptation
attention
Context
Domain adaptation
Domains
Feature extraction
Generative adversarial networks
Generators
Meteorology
Modules
multiple source and target domains
Semantics
Task analysis
Visualization
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Summary:Domain adaptation aims to alleviate the distribution discrepancy between source and target domains. Most conventional methods focus on one target domain setting adapted from one or multiple source domains while neglecting the multi-target domain setting. We argue that different target domains also have complementary information, which is very important for performance improvement. In this paper, we propose an Attention-guided Multiple source-and-target Domain Adaptation (AMDA) method to capture the context dependency information on transferable regions among multiple source and target domains. The innovation points of this paper are as follows: (1) We use numerous adversarial strategies to harvest sufficient information from multiple source and target domains, which extends the generalization and robustness of the feature pools. (2) We propose an intra-domain and inter-domain attention module to explore transferable context information. The proposed attention module can learn domain-invariant representations and reduce the negative transfer by focusing on transferable knowledge. Extensive experiments validate the effectiveness of our method with achieving state-of-the-art performance on several unsupervised domain adaptation datasets.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2020.3031161