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Open AccessArticle

Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
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Appl. Sci. 2018, 8(12), 2529; https://doi.org/10.3390/app8122529
Received: 9 November 2018 / Revised: 27 November 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
(This article belongs to the Special Issue Advances in Deep Learning)
When large-scale annotated data are not available for certain image classification tasks, training a deep convolutional neural network model becomes challenging. Some recent domain adaptation methods try to solve this problem using generative adversarial networks and have achieved promising results. However, these methods are based on a shared latent space assumption and they do not consider the situation when shared high level representations in different domains do not exist or are not ideal as they assumed. To overcome this limitation, we propose a neural network structure called coupled generative adversarial autoencoders (CGAA) that allows a pair of generators to learn the high-level differences between two domains by sharing only part of the high-level layers. Additionally, by introducing a class consistent loss calculated by a stand-alone classifier into the generator optimization, our model is able to generate class invariant style-transferred images suitable for classification tasks in domain adaptation. We apply CGAA to several domain transferred image classification scenarios including several benchmark datasets. Experiment results have shown that our method can achieve state-of-the-art classification results. View Full-Text
Keywords: unsupervised domain adaptation; generative adversarial networks; autoencoder unsupervised domain adaptation; generative adversarial networks; autoencoder
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Wang, X.; Wang, X. Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders. Appl. Sci. 2018, 8, 2529.

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