  Generative Adversarial Networks (GANs) are formulated as minimax game
problems, whereby generators attempt to approach real data distributions by
virtue of adversarial learning against discriminators. The intrinsic problem
complexity poses the challenge to enhance the performance of generative
networks. In this work, we aim to boost model learning from the perspective of
network architectures, by incorporating recent progress on automated
architecture search into GANs. To this end, we propose a fully differentiable
search framework for generative adversarial networks, dubbed alphaGAN. The
searching process is formalized as solving a bi-level minimax optimization
problem, in which the outer-level objective aims for seeking a suitable network
architecture towards pure Nash Equilibrium conditioned on the generator and the
discriminator network parameters optimized with a traditional GAN loss in the
inner level. The entire optimization performs a first-order method by
alternately minimizing the two-level objective in a fully differentiable
manner, enabling architecture search to be completed in an enormous search
space. Extensive experiments on CIFAR-10 and STL-10 datasets show that our
algorithm can obtain high-performing architectures only with 3-GPU hours on a
single GPU in the search space comprised of approximate 2 ? 1011 possible
configurations. We also provide a comprehensive analysis on the behavior of the
searching process and the properties of searched architectures, which would
benefit further research on architectures for generative models. Pretrained
models and codes are available at https://github.com/yuesongtian/AlphaGAN.
