MATLAB PROJECT
Asymmetric GAN for
Unpaired Image-to-Image Translation
Abstract:
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.
No comments:
Post a Comment