Abstract
The neural architecture search (NAS) method provides a new approach to the design of generative adversarial network (GAN)’s architecture. The existing state-of-the-art algorithm, AutoGAN, is an example in discovering GAN’s architecture using reinforcement learning (RL)-based NAS. However, performance differences between the candidate architectures are not taken into consideration. In this paper, one new ImprovedAutoGAN algorithm based on AutoGAN is proposed. We found that which candidate architectures to use can affect the performance of the final network. So in the process of architecture search, compared with AutoGAN, in which candidate architectures are selected randomly, our method uses gradient bandit algorithm to increase the probability of selecting networks with better performance. This paper also introduces a temperature coefficient in the algorithm to prevent the search results from getting trapped in the local optimum. The GANs are searched using the same search space as AutoGAN, and the discovered GAN has a Frechet inception distance (FID) score of 11.60 on CIFAR-10, reaching the best level in the current RL-based NAS methods. Experiments also show that the transferability of this GAN is satisfying.
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Notes
In Liu et al. (2018), the term “cell” is called “block.” Here, we use “cell” instead in order to be consistent with AutoGAN.
In stage 1, the generator is composed of one cell, so there is no candidate architectures and the operation to select it.
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Acknowledgements
The authors are grateful to the College of Computer Science, Nanjing University of Posts and Telecommunications for kindly providing the required computational resources. We also thank Baofeng Zhang for his help with this Project. This study was funded by National Natural Science Foundation of China (NSFC) 2019-2022 (Grants 61877051 and 61872079). Associate Professor Jun Shen was also supported by research exchange program funded University Global Partnership Network.
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This study was funded by National Natural Science Foundation of China (NSFC) 2019-2022 (Grants 61877051 and 61872079).
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Fan, Y., Zhou, G., Shen, J. et al. Toward gradient bandit-based selection of candidate architectures in AutoGAN. Soft Comput 25, 4367–4378 (2021). https://doi.org/10.1007/s00500-020-05446-x
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DOI: https://doi.org/10.1007/s00500-020-05446-x