Multi-SelfGAN: A Self-Guiding Neural Architecture Search Method for Generative Adversarial Networks with Multi-Controllers

Publication Name

IEEE Transactions on Cognitive and Developmental Systems


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 words, we propose to self-guide the search process. In the mean time, we introduce new concept of multiple controllers and the method of reward shaping to independently and effectively search the cell architectures. The experiments demonstrate the effectiveness and efficiency of our Multi-Self GAN and the ablation study also exhibits its robustness.

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