# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders  
[PERSON]  
1 State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; 1 [EMAIL_ADDRESS]  
[PERSON]  
1 State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; 1 [EMAIL_ADDRESS]  
Footnote 1: [[https://github.com](https://github.com)]([https://github.com](https://github.com))  
volume to train a more robust image classifier [15; 16; 17]. In these methods, the training strategy of the final classifier need to be carefully designed since the newly generated images have no label. The other type of approaches generate labeled target domain images directly by transferring the source domain images into target domain style and have achieved some state-of-the-art results, such as CoGAN [18] and UNIT [19]. These methods are based on the shared latent space assumption, which assumes that the differences of the source domain and the target domain are primarily low-level, and that the two domains share a common high-level latent space. This assumption works well for simple scenarios such as digits adaptation between MNIST [20] and USPS [21] but faces challenges when the semantic features are more complex. When shared high-level latent space in different domains does not exist or such latent space is not as ideal as assumed, these methods will fail [18].  
In this paper, we propose an unsupervised domain adaptation method for image classification by combining generative adversarial networks with autoencoders. We call our proposed network architecture Coupled Generative Adversarial Autoencoders (CGAA). Our work is perhaps most similar to CoGAN and UNIT, but we try to solve the aforementioned shortcomings of these methods by the following designs: CGAA consists of a pair of generative adversarial networks (GAN) and a domain adaptive classifier. The architecture of the generator in GAN is designed based on the autoencoder. During training, part of the layers in the generators are forced to share their weights, which gives our model the ability to learn the domain transformation in an unsupervised manner and generate synthetic target domain images with label. By decoupling the highest level layer, we give our model the capacity to tolerant the differences of high-level features between the domains. The classifier provides a class-invariant loss to help the generator produce more suitable images for the classification task in domain adaptation. The main contributions of this work are:  
* We propose an unsupervised domain adaptation method for image classification. Our method trains a pair of coupled generative adversarial networks in which the generator has an encoder-decoder structure.
* We force part of the layers in the generator to share weights during training to generate labeled synthetic images, and make the highest level layer decoupled for different high-level representations.
* We introduce a class consistent loss into the GAN training, which is calculated from the output of a stand-alone domain adaptive classifier. It can help the generator to generate more suitable images for domain adaptation.