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.

本文提出了“跨领域对抗自编码器（CDAAE）”来解决跨领域图像推理、生成和转换的问题，实现了基于内容的不同领域图片的生成，并且可以通过有标签的和无标签的数据进行训练，同时在数据集SVHN，MNIST和CASIA上都获得了具有视觉吸引力的图像生成效果和优越的领域适应性。

跨域对抗自编码器