Abstract
In this chapter we briefly review the main classes of approaches that were developed prior to the surge of deep learning for DA. Our goal here is not to provide an exhaustive discussion of such traditional methods, as several excellent surveys on the topic have already been published [Csurka, 2017, Gopalan et al., 2015, Venkateswara and Panchanathan, 2020, Weiss et al., 2016, Zhang and Gao, 2019, Zhuang et al., 2019]. Instead, we rather aim to highlight the main trends in traditional DA, and, in Chapter 4, draw connections between these approaches and the deep learning ones.
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Csurka, G., Hospedales, T.M., Salzmann, M., Tommasi, T. (2022). Traditional Methods. In: Visual Domain Adaptation in the Deep Learning Era. Synthesis Lectures on Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-031-79175-8_3
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DOI: https://doi.org/10.1007/978-3-031-79175-8_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-79170-3
Online ISBN: 978-3-031-79175-8
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