  Recent advances of image-to-image translation focus on learning the
one-to-many mapping from two aspects: multi-modal translation and multi-domain
translation. However, the existing methods only consider one of the two
perspectives, which makes them unable to solve each other's problem. To address
this issue, we propose a novel unified model, which bridges these two
objectives. First, we disentangle the input images into the latent
representations by an encoder-decoder architecture with a conditional
adversarial training in the feature space. Then, we encourage the generator to
learn multi-mappings by a random cross-domain translation. As a result, we can
manipulate different parts of the latent representations to perform multi-modal
and multi-domain translations simultaneously. Experiments demonstrate that our
method outperforms state-of-the-art methods.
