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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources

February 1, 2023

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Authors

Yongchun Zhu

Chinese Academy of Sciences


Fuzhen Zhuang

Chinese Academy of Sciences


Deqing Wang

Beihang University


DOI:

10.1609/aaai.v33i01.33015989


Abstract:

While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Adaptation (SUDA). However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other. Thus, domain adapters from multiple sources should not be modeled in the same way. Recent deep learning based Multi-source Unsupervised Domain Adaptation (MUDA) algorithms focus on extracting common domain-invariant representations for all domains by aligning distribution of all pairs of source and target domains in a common feature space. However, it is often very hard to extract the same domain-invariant representations for all domains in MUDA. In addition, these methods match distributions without considering domain-specific decision boundaries between classes. To solve these problems, we propose a new framework with two alignment stages for MUDA which not only respectively aligns the distributions of each pair of source and target domains in multiple specific feature spaces, but also aligns the outputs of classifiers by utilizing the domainspecific decision boundaries. Extensive experiments demonstrate that our method can achieve remarkable results on popular benchmark datasets for image classification.

Topics: AAAI

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Yongchun Zhu||Fuzhen Zhuang||Deqing Wang Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources Proceedings of the AAAI Conference on Artificial Intelligence (2019) 5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources AAAI 2019, 5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang (2019). Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources. Proceedings of the AAAI Conference on Artificial Intelligence, 5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang. Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang. 2019. Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources. "Proceedings of the AAAI Conference on Artificial Intelligence". 5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang. (2019) "Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources", Proceedings of the AAAI Conference on Artificial Intelligence, p.5989-5996

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang, "Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources", AAAI, p.5989-5996, 2019.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang. "Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang. "Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 5989-5996.

Yongchun Zhu||Fuzhen Zhuang||Deqing Wang. Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources. AAAI[Internet]. 2019[cited 2023]; 5989-5996.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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