  Conditional Generative Adversarial Networks (cGAN) were designed to generate
images based on the provided conditions, \eg, class-level distributions.
However, existing methods have used the same generating architecture for all
classes. This paper presents a novel idea that adopts NAS to find a distinct
architecture for each class. The search space contains regular and
class-modulated convolutions, where the latter is designed to introduce
class-specific information while avoiding the reduction of training data for
each class generator. The search algorithm follows a weight-sharing pipeline
with mixed-architecture optimization so that the search cost does not grow with
the number of classes. To learn the sampling policy, a Markov decision process
is embedded into the search algorithm and a moving average is applied for
better stability. We evaluate our approach on CIFAR10 and CIFAR100. Besides
achieving better image generation quality in terms of FID scores, we discover
several insights that are helpful in designing cGAN models. Code is available
at https://github.com/PeterouZh/NAS_cGAN.
