  Recent studies have shown remarkable success in unsupervised image-to-image
translation. However, if there has no access to enough images in target
classes, learning a mapping from source classes to the target classes always
suffers from mode collapse, which limits the application of the existing
methods. In this work, we propose a zero-shot unsupervised image-to-image
translation framework to address this limitation, by associating categories
with their side information like attributes. To generalize the translator to
previous unseen classes, we introduce two strategies for exploiting the space
spanned by the semantic attributes. Specifically, we propose to preserve
semantic relations to the visual space and expand attribute space by utilizing
attribute vectors of unseen classes, thus encourage the translator to explore
the modes of unseen classes. Quantitative and qualitative results on different
datasets demonstrate the effectiveness of our proposed approach. Moreover, we
demonstrate that our framework can be applied to many tasks, such as zero-shot
classification and fashion design.
