  Recent progress in Generative Adversarial Networks (GANs) has shown promising
signs of improving GAN training via architectural change. Despite some early
success, at present the design of GAN architectures requires human expertise,
laborious trial-and-error testings, and often draws inspiration from its image
classification counterpart. In the current paper, we present the first neural
architecture search algorithm, automated neural architecture search for deep
generative models, or AGAN for abbreviation, that is specifically suited for
GAN training. For unsupervised image generation tasks on CIFAR-10, our
algorithm finds architecture that outperforms state-of-the-art models under
same regularization techniques. For supervised tasks, the automatically
searched architectures also achieve highly competitive performance,
outperforming best human-invented architectures at resolution $32\times32$.
Moreover, we empirically demonstrate that the modules learned by AGAN are
transferable to other image generation tasks such as STL-10.
