  Image-to-image translation aims to learn the mapping between two visual
domains. There are two main challenges for this task: 1) lack of aligned
training pairs and 2) multiple possible outputs from a single input image. In
this work, we present an approach based on disentangled representation for
generating diverse outputs without paired training images. To synthesize
diverse outputs, we propose to embed images onto two spaces: a domain-invariant
content space capturing shared information across domains and a domain-specific
attribute space. Our model takes the encoded content features extracted from a
given input and attribute vectors sampled from the attribute space to
synthesize diverse outputs at test time. To handle unpaired training data, we
introduce a cross-cycle consistency loss based on disentangled representations.
Qualitative results show that our model can generate diverse and realistic
images on a wide range of tasks without paired training data. For quantitative
evaluations, we measure realism with user study and Fr\'{e}chet inception
distance, and measure diversity with the perceptual distance metric,
Jensen-Shannon divergence, and number of statistically-different bins.
