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.

翻译：我们开发了一种针对CycleGAN的神经架构搜索（NAS）框架，用于执行无配对图像到图像的翻译任务。由于任务差异和搜索空间更大，将先前用于生成对抗网络（GANs）的NAS技术直接扩展到CycleGAN并非易事。我们设计了由简单的基于ResNet的细胞堆叠而成的架构，并开发了一种能有效探索大规模搜索空间的搜索方法。我们证明，所提出的CycleGANAS框架不仅能有效发现性能可媲美或超越原始CycleGAN的高效架构，还能通过为每个翻译方向单独进行架构搜索，成功解决数据不平衡问题。据我们所知，这是首个针对CycleGAN的NAS研究成果，也为更复杂结构的NAS研究提供了启示。