Maximum Density Divergence for Domain Adaptation

Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 43; H. 11; S. 3918 - 3930
Hauptverfasser: Li, Jingjing, Chen, Erpeng, Ding, Zhengming, Zhu, Lei, Lu, Ke, Shen, Heng Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation
Adaptation models
adversarial learning
Benchmark testing
Density
Domain adaptation
Domains
Empirical analysis
Games
Kernel
Learning
Measurement
Performance evaluation
Task analysis
Training
transfer learning
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung:Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.2991050