  Generative adversarial networks (GANs) have proven successful in image
generation tasks. However, GAN training is inherently unstable. Although many
works try to stabilize it by manually modifying GAN architecture, it requires
much expertise. Neural architecture search (NAS) has become an attractive
solution to search GANs automatically. The early NAS-GANs search only
generators to reduce search complexity but lead to a sub-optimal GAN. Some
recent works try to search both generator (G) and discriminator (D), but they
suffer from the instability of GAN training. To alleviate the instability, we
propose an efficient two-stage evolutionary algorithm-based NAS framework to
search GANs, namely EAGAN. We decouple the search of G and D into two stages,
where stage-1 searches G with a fixed D and adopts the many-to-one training
strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts
the one-to-one training and weight-resetting strategies to enhance the
stability of GAN training. Both stages use the non-dominated sorting method to
produce Pareto-front architectures under multiple objectives (e.g., model size,
Inception Score (IS), and Fr\'echet Inception Distance (FID)). EAGAN is applied
to the unconditional image generation task and can efficiently finish the
search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve
competitive results (IS=8.81$\pm$0.10, FID=9.91) on the CIFAR-10 dataset and
surpass prior NAS-GANs on the STL-10 dataset (IS=10.44$\pm$0.087, FID=22.18).
Source code: https://github.com/marsggbo/EAGAN.

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing