"EfficientAutoGAN: Predicting the Rewards in Reinforcement-Based Neural" by Yi Fan, Xiulian Tang et al.
This article is inspired by human's memory and recognition process to improve neural architecture search (NAS), which has shown novelty and significance in the design of generative adversarial networks (GANs), but the extremely enormous time consumption for searching GAN architectures based on reinforcement learning (RL) limits its applicability to a great extent. The main reason behind the challenge is that, the performance evaluation of subnetworks during the search process takes too much time. To solve this problem, we propose a new algorithm, EfficientAutoGAN, in which a graph convolu...
Source: uow.edu.au