  We develop a Neural Architecture Search (NAS) framework for CycleGAN that
carries out unpaired image-to-image translation task. Extending previous NAS
techniques for Generative Adversarial Networks (GANs) to CycleGAN is not
straightforward due to the task difference and greater search space. We design
architectures that consist of a stack of simple ResNet-based cells and develop
a search method that effectively explore the large search space. We show that
our framework, called CycleGANAS, not only effectively discovers
high-performance architectures that either match or surpass the performance of
the original CycleGAN, but also successfully address the data imbalance by
individual architecture search for each translation direction. To our best
knowledge, it is the first NAS result for CycleGAN and shed light on NAS for
more complex structures.
