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Publication Details Fan, Y., Tang, X., Zhou, G. & Shen, J. (2020). Efficient AutoGAN: Predicting the rewards in reinforcement-based neural architecture search for Generative Adversarial Networks. IEEE Transactions on Cognitive and Developmental Systems, online first 1-13. Abstract This paper 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 (GAN), 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 sub-networks during the search process takes too much time. To solve this problem, we propose a new algorithm, EfficientAutoGAN, in which a Graph Convolution Network (GCN) predictor is introduced to predict the performance of sub-networks instead of formally assessing or

Graph convolution networkGenerative adversarial networksNeural architecture searchநரம்பியல் கட்டிடக்கலை தேடல்