Network architecture search (NAS) achieves state-of-the-art results in
various tasks such as classification and semantic segmentation. Recently, a
reinforcement learning-based approach has been proposed for Generative
Adversarial Networks (GANs) search. In this work, we propose an alternative
strategy for GAN search by using a method called DEGAS (Differentiable
Efficient GenerAtor Search), which focuses on efficiently finding the generator
in the GAN. Our search algorithm is inspired by the differential architecture
search strategy and the Global Latent Optimization (GLO) procedure. This leads
to both an efficient and stable GAN search. After the generator architecture is
found, it can be plugged into any existing framework for GAN training. For
CTGAN, which we use in this work, the new model outperforms the original
inception score results by 0.25 for CIFAR-10 and 0.77 for STL. It also gets
better results than the RL based GAN search methods in shorter search time.

本研究提出了一种名为 DEGAS 的新型策略，旨在通过差分架构搜索策略和全局潜在优化过程，有效地找到对抗生成网络（GAN）中的生成器，在此基础上，在 CTGAN 模型上优于原始成果，并在较短的搜索时间内比基于强化学习的 GAN 搜索方法获得更好的结果.