东华大学学报(英文版)

2020, v.37(02) 121-129

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Generative Adversarial Network with Separate Learning Rule for Image Generation
Generative Adversarial Network with Separate Learning Rule for Image Generation

印峰;陈新雨;邱杰;康永亮;

摘要(Abstract):

Boundary equilibrium generative adversarial networks(BEGANs) are the improved version of generative adversarial networks(GANs). In this paper, an improved BEGAN with a skip-connection technique in the generator and the discriminator is proposed. Moreover, an alternative time-scale update rule is adopted to balance the learning rate of the generator and the discriminator. Finally, the performance of the proposed method is quantitatively evaluated by Fréchet inception distance(FID) and inception score(IS). The test results show that the performance of the proposed method is better than that of the original BEGAN.

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基金项目(Foundation): National Natural Science Foundation of China(Nos.61602398 and U19A2083);; Science and Technology Department of Hunan Province,China(No.2019GK4007)

作者(Author): 印峰;陈新雨;邱杰;康永亮;

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