PARIS, June 13 To many they are art’s next big thing digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a.
To many they are art s next big thing - digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism.
To many they are art's next big thing digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism.
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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.