Survey of Universal Domain Adaptation

He Qiuyan; Deng Minghua

doi:10.7544/issn1000-1239.202220935

Journal of Computer Research and Development > 2024 > 61(1): 120-144. > DOI: 10.7544/issn1000-1239.202220935

He Qiuyan, Deng Minghua. Survey of Universal Domain Adaptation[J]. Journal of Computer Research and Development, 2024, 61(1): 120-144. DOI: 10.7544/issn1000-1239.202220935

Citation:

He Qiuyan, Deng Minghua. Survey of Universal Domain Adaptation[J]. Journal of Computer Research and Development, 2024, 61(1): 120-144. DOI: 10.7544/issn1000-1239.202220935

Citation:

He Qiuyan, Deng Minghua. Survey of Universal Domain Adaptation[J]. Journal of Computer Research and Development, 2024, 61(1): 120-144. DOI: 10.7544/issn1000-1239.202220935

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Survey of Universal Domain Adaptation

He Qiuyan^1,,
Deng Minghua^{1, 2, 3, ,}

1.
School of Mathematical Sciences, Peking University, Beijing 100871
2.
Center for Statistical Science, Peking University, Beijing 100871
3.
Center for Quantitative Biology, Peking University, Beijing 100871

Funds: This work was supported by the National Key Research and Development Program of China (2021YFF1200902) and the National Natural Science Foundation of China (32270689,12126305).

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Author Bio:
He Qiuyan: born in 1997. PhD candidate. Her main research interests include bioinformatics and machine learning

Deng Minghua: born in 1969. PhD, professor, PhD supervisor. His main research interests include bioinformatics and computational biology
Received Date: November 10, 2022
Revised Date: May 14, 2023
Available Online: September 20, 2023

Graphical Abstract

Abstract

Abstract

Domain adaptation relaxes the assumption that test data follows the same distribution as training data in traditional machine learning, and transfers knowledge from label-rich source domain to label-scarce target domain when domain bias exists. However, most domain adaptation methods make assumptions about the relative relationship between the label space of source domain and target domain which limits the application of these methods in reality. Therefore, universal domain adaptation is proposed to label target data without prior information of label spaces of source domain and target domain. Generally, a universal domain adaptation model needs to solve several problems. Firstly, it is desired to judge whether a target sample belongs to classes in source domain. Then it ought to transfer knowledge of common classes shared by source domain and target domain to label target samples, as well as dealing with the domain bias and potential category shift. Due to its practicability, universal domain adaptation has gained more and more attention now. Many branches of universal domain adaptation have been proposed, and researchers are trying to apply it onto datasets different from images. In this review, we conduct an exhaustive survey of universal domain adaptation methods till now from the point of problem setting and strategies. Then we compare typical methods and analyze the difficulties in the universal domain adaptation. After that, we introduce current applications of universal domain adaptation. Meanwhile, we analyze relevant research areas briefly. Lastly, we discuss the problems of universal domain adaptation that can be studied further.
- universal domain adaptation,
- transfer learning,
- adversarial learning,
- contrastive learning,
- self-training

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References (75)

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