Cross-Domain Adversarial Autoencoder for Fine Grained Category Preserving Image Translation

Abstract

Cross-domain image translation attempt to translate images from one domain to another domain, with the content of images preserved. Current approaches treat image's content as the underlying spatial structure, and translation only change image's style of color and texture. These methods can generate realistic results, but may not be able to preserve image's fine grained semantic category information and suffer from the lack of diversity in objects' shapes and viewing angles. In this paper, we propose the problem of fine grained category preserving image translation that aims at preserving image's fine grained category information in cross-domain translation. A novel framework called Cross-Domain Adversarial AutoEncoder (CDAAE) is proposed to solve the problem. CDAAE assumes that cross-domain images have shared content-latent-code space and separate style-latent-code spaces. The content latent code encodes image's basic category information, while the style latent code represents other domain-specific properties, including color, texture, shape, etc. Our experiments evaluate models from aspects of image's quality, diversity as well as category preserving ability, showing CDAAE's advantages over current methods. We also design an algorithm to apply CDAAE to domain adaptation. Experiments on benchmark datasets demonstrate that the proposed method achieves state-of-the-art results.