  In this paper, we propose the Cross-Domain Adversarial Auto-Encoder (CDAAE)
to address the problem of cross-domain image inference, generation and
transformation. We make the assumption that images from different domains share
the same latent code space for content, while having separate latent code space
for style. The proposed framework can map cross-domain data to a latent code
vector consisting of a content part and a style part. The latent code vector is
matched with a prior distribution so that we can generate meaningful samples
from any part of the prior space. Consequently, given a sample of one domain,
our framework can generate various samples of the other domain with the same
content of the input. This makes the proposed framework different from the
current work of cross-domain transformation. Besides, the proposed framework
can be trained with both labeled and unlabeled data, which makes it also
suitable for domain adaptation. Experimental results on data sets SVHN, MNIST
and CASIA show the proposed framework achieved visually appealing performance
for image generation task. Besides, we also demonstrate the proposed method
achieved superior results for domain adaptation. Code of our experiments is
available in https://github.com/luckycallor/CDAAE.
