  Learning inter-domain mappings from unpaired data can improve performance in
structured prediction tasks, such as image segmentation, by reducing the need
for paired data. CycleGAN was recently proposed for this problem, but
critically assumes the underlying inter-domain mapping is approximately
deterministic and one-to-one. This assumption renders the model ineffective for
tasks requiring flexible, many-to-many mappings. We propose a new model, called
Augmented CycleGAN, which learns many-to-many mappings between domains. We
examine Augmented CycleGAN qualitatively and quantitatively on several image
datasets.
