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A survey of deep domain adaptation based on label set classification

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Abstract

Traditional machine learning requires good tags to obtain excellent performance, while manual tagging usually consumes a lot of time and money. Due to the influence of domain shift, using the trained model on the source domain directly on the target domain is not good. Domain adaptation is used to solve the above problems. The deep domain adaptation method uses deep neural networks to complete domain adaptation. This article has carried out a comprehensive review of the deep domain adaptation method of image classification. The main contributions are the following four aspects. Firstly, we divided the deep domain adaptation into several categories based on the label set of the source domain and the target domain. Secondly, we summarized various methods of Closed-set domain adaptation. Thirdly, we discussed current methods of multi-source domain adaptation. Finally, we discussed future research directions, challenges, and possible solutions.

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Acknowledgments

This work is partly supported by the Natural Science Foundation of China (Grant No. 62006127, 61833011 and 62073173), partly supported by NUPTSF under Grant NY218120 and Grant NY220021, and partly supported by Jiangsu Shuang-Chuang Project under Grant CZ005SC19019 and Nanjing Overseas Innovation Project Grant RK005NLX20001. It is also supported by National Science Foundation of Jiangsu Province, China (Grant No. BK20191376 and BK20190728).

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  1. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Min Fan, Ziyun Cai, Tengfei Zhang & Baoyun Wang

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Fan, M., Cai, Z., Zhang, T. et al. A survey of deep domain adaptation based on label set classification. Multimed Tools Appl 81, 39545–39576 (2022). https://doi.org/10.1007/s11042-022-12630-8

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