  Unpaired image-to-image translation problem aims to model the mapping from
one domain to another with unpaired training data. Current works like the
well-acknowledged Cycle GAN provide a general solution for any two domains
through modeling injective mappings with a symmetric structure. While in
situations where two domains are asymmetric in complexity, i.e., the amount of
information between two domains is different, these approaches pose problems of
poor generation quality, mapping ambiguity, and model sensitivity. To address
these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric
domains by introducing an auxiliary variable (aux) to learn the extra
information for transferring from the information-poor domain to the
information-rich domain, which improves the performance of state-of-the-art
approaches in the following ways. First, aux better balances the information
between two domains which benefits the quality of generation. Second, the
imbalance of information commonly leads to mapping ambiguity, where we are able
to model one-to-many mappings by tuning aux, and furthermore, our aux is
controllable. Third, the training of Cycle GAN can easily make the generator
pair sensitive to small disturbances and variations while our model decouples
the ill-conditioned relevance of generators by injecting aux during training.
We verify the effectiveness of our proposed method both qualitatively and
quantitatively on asymmetric situation, label-photo task, on Cityscapes and
Helen datasets, and show many applications of asymmetric image translations. In
conclusion, our AsymGAN provides a better solution for unpaired image-to-image
translation in asymmetric domains.
