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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation

February 1, 2023

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Authors

Chao Chen

Zhejiang University


Zhihang Fu

Alibaba Group


Zhihong Chen

Zhejiang University


Sheng Jin

Harbin Institute of Technology


Zhaowei Cheng

Zhejiang University


Xinyu Jin

Zhejiang University


Xian-sheng Hua

Alibaba Group


DOI:

10.1609/aaai.v34i04.5745


Abstract:

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, HoMM (order≥ 3) is expected to perform fine-grained domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at https://github.com/chenchao666/HoMM-Master

Topics: AAAI

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HOW TO CITE:

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation AAAI 2020, 3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua (2020). HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua. HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua. 2020. HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation. "Proceedings of the AAAI Conference on Artificial Intelligence". 3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua. (2020) "HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation", Proceedings of the AAAI Conference on Artificial Intelligence, p.3422-3429

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua, "HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation", AAAI, p.3422-3429, 2020.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua. "HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua. "HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 3422-3429.

Chao Chen||Zhihang Fu||Zhihong Chen||Sheng Jin||Zhaowei Cheng||Xinyu Jin||Xian-sheng Hua. HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation. AAAI[Internet]. 2020[cited 2023]; 3422-3429.


ISSN: 2374-3468


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