|Table of Contents|

Iterative cost function and variable parameter generativeadversarial networks(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年01期
Page:
35-
Research Field:
Publishing date:

Info

Title:
Iterative cost function and variable parameter generativeadversarial networks
Author(s):
Chen Yao1Song Xiaoning1Yu Dongjun2
1.School of IoT Engineering,Jiangnan University,Wuxi 214122,China; 2.School of ComputerScience and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
generative adversarial networks iterative cost function method variable parameter distribution distance
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2019.43.01.005
Abstract:
In order to solve the difficult training problem of generative adversarial networks,this paper proposes an iterative cost function and variable parameter generative adversarial networks based on the Wasserstein GAN(WGAN)method. For the improvement of penalty items in the original WGAN,iterative methods are used to increase penalty instead of the original randomly selected method. Aiming at the hyper-parameter of penalty item of fixed cost function in WGAN,the strategy of changing hyper-parameter is put forward. The change is based on the distance between imitation distribution and real distribution. Experiments conducted on MNIST handwritten font datasets and CELEBA face datasets show the effectiveness of the proposed method as compared with the traditional WGAN,significantly improving the convergence speed of the generator.

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Last Update: 2019-02-28