• DOI:10.1016/j.neucom.2018.05.083
Corpus ID: 3654323

Deep Visual Domain Adaptation: A Survey

@article{Wang2018DeepVD,
  title={Deep Visual Domain Adaptation: A Survey},
  author={M. Wang and W. Deng},
  journal={Neurocomputing},
  year={2018},
  volume={312},
  pages={135-153}
}

M. Wang, W. Deng

Published 2018

Computer Science

Neurocomputing

Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. [...] Key Method First, we present a taxonomy of different deep domain adaption scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaption approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision…Expand Abstract

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