Open AccessAdversarial auto‐encoder for unsupervised deep domain adaptation
Author(s) -
Shao Rui,
Lan Xiangyuan
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6687
Subject(s) - computer science , artificial intelligence , adversarial system , classifier (uml) , autoencoder , encoder , pattern recognition (psychology) , domain adaptation , domain (mathematical analysis) , unsupervised learning , deep learning , adaptation (eye) , machine learning , mathematics , mathematical analysis , physics , optics , operating system
Unsupervised visual domain adaptation aims to train a classifier that works well on a target domain given labelled source samples and unlabelled target samples. The key issue in unsupervised visual domain adaptation is how to do the feature alignment between source and target domains. Inspired by the adversarial learning in generative adversarial networks, this study proposes a novel adversarial auto‐encoder for unsupervised deep domain adaptation. This method incorporates the auto‐encoder with the adversarial learning so that the domain similarity and reconstruction information from the decoder can be exploited to facilitate the adversarial domain adaptation in the encoder. Extensive experiments on various visual recognition tasks show that the proposed method performs favourably against or better than competitive state‐of‐the‐art methods.