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Unsupervised domain adaptation with adversarial distribution adaptation network

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Abstract

Adversarial domain adaptation is a powerful approach to transfer the knowledge of the label-rich source domain to the label-scarce target domain by mitigating domain shifts across distributions. Existing domain adaptation methods align either the marginal distribution with a single-domain discriminator or conditional distributions with multiple-domain discriminators. However, aligning both marginal (global) and conditional (local) distributions should be considered for domain adaptation. This paper proposes a novel adversarial distribution adaptation network (ADAN) to jointly reduce both the global and local distribution discrepancies between different domains for learning domain-invariant representations. ADAN utilizes a single-domain discriminator to adapt the global distribution between two domains, and source decision boundaries to align the local distributions between sub-domains. Furthermore, we extend our ADAN as improved ADAN (iADAN), in which we utilize a feature norm term to regularize the task-specific features to improve model generalization. Extensive experimental results show that our method outperforms other state-of-the-art domain adaptation methods on Office-Home and ImageCLEF-DA datasets.

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Data availibility

Datasets are open source and can be obtained through website links or references.

Notes

  1. https://www.imageclef.org/2014/adaptation.

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities under Grant 2019XD-A20.

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Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qiang Zhou, Wen’an Zhou & Shirui Wang

  2. School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Ying Xing

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  1. Qiang Zhou
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  2. Wen’an Zhou
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  3. Shirui Wang
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Correspondence to Wen’an Zhou.

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Zhou, Q., Zhou, W., Wang, S. et al. Unsupervised domain adaptation with adversarial distribution adaptation network. Neural Comput & Applic 33, 7709–7721 (2021). https://doi.org/10.1007/s00521-020-05513-2

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