  Recently image-to-image translation has attracted significant interests in
the literature, starting from the successful use of the generative adversarial
network (GAN), to the introduction of cyclic constraint, to extensions to
multiple domains. However, in existing approaches, there is no guarantee that
the mapping between two image domains is unique or one-to-one. Here we propose
a self-inverse network learning approach for unpaired image-to-image
translation. Building on top of CycleGAN, we learn a self-inverse function by
simply augmenting the training samples by swapping inputs and outputs during
training and with separated cycle consistency loss for each mapping direction.
The outcome of such learning is a proven one-to-one mapping function. Our
extensive experiments on a variety of datasets, including cross-modal medical
image synthesis, object transfiguration, and semantic labeling, consistently
demonstrate clear improvement over the CycleGAN method both qualitatively and
quantitatively. Especially our proposed method reaches the state-of-the-art
result on the cityscapes benchmark dataset for the label to photo unpaired
directional image translation.
