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JBE, vol. 27, no. 4, pp.511-518, July, 2022

DOI: https://doi.org/10.5909/JBE.2022.27.4.511

Deep Learning based Domain Adaptation: A Survey

Jaemin Na and Wonjun Hwang

C.A E-mail: wjhwang@ajou.ac.kr

Abstract:

Supervised learning based on deep learning has made a leap forward in various application fields. However, many supervised learning methods work under the common assumption that training and test data are extracted from the same distribution. If it deviates from this constraint, the deep learning network trained in the training domain is highly likely to deteriorate rapidly in the test domain due to the distribution difference between domains. Domain adaptation is a methodology of transfer learning that trains a deep learning network to make successful inferences in a label-poor test domain (i.e., target domain) based on learned knowledge of a labeled-rich training domain (i.e., source domain). In particular, the unsupervised domain adaptation technique deals with the domain adaptation problem by assuming that only image data without labels in the target domain can be accessed. In this paper, we explore the unsupervised domain adaptation techniques.



Keyword: Deep Learning, Unsupervised Domain Adaptation

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