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Domain Adaptation: A Survey

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

In computer vision, domain shifts are a typical issue. A classifier that has been trained on a source domain will not be able to perform well on a target domain. As a result, a source classifier taught to discriminate based on a particular distribution will struggle to classify new data from a different distribution. Domain adaptation is a hot area of research due to the plethora of applications available from this technique. Many developments have been made in this direction in recent decades. In light of this, we have compiled a summary of domain adaptation research, concentrating on work done in the last few years (2015–2022) for the benefit of the research community. We have categorically placed the important research works in DA under the chosen methodologies and have critically assessed the performances of these techniques. The study covers these features at length, and thorough descriptions of representative methods for each group are provided.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, 690525, India

    Ashly Ajith & G. Gopakumar

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  1. Ashly Ajith
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  2. G. Gopakumar
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Correspondence to Ashly Ajith .

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Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ajith, A., Gopakumar, G. (2023). Domain Adaptation: A Survey. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_47

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