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In the existing reinforcement learning (RL)-based neural architecture search (NAS) methods for a generative adversarial network (GAN), both the generator and the discriminator architecture are usually treated as the search objects. In this article, we take a different perspective to propose an approach by treating the generator as the search objective and the discriminator as the judge to evaluate the performance of the generator architecture. Consequently, we can convert this NAS problem to a GAN-style problem, similar to using a controller to generate sequential data via reinforcement learni...

In recent years, Reinforcement Learning and Gradient optimization were applied with Neural Architecture Search algorithms in Generative Adversarial Network to achieve their state-of-the-art (SOTA) performance. However, the existing RL-based methods utilised the calculation of Inception Score or Fréchet Inception Distance as the reward value to guide the controller, which actually wasted much of searching time. In order to improve the search efficiency without degradation of performance, this paper proposes recycling the discriminator to evaluate the performance of architectures, in other word...