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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 12
3607-3611

GAN based on variable loss and manifold regularization

Ding Saisaia,b
Lyu Jiaa,b
a. College of Computer & Information Sciences, b. Chongqing Center of Engineering Technology Research on Digital Agriculture Service, Chongqing Normal University, Chongqing 401331, China
DOI: 10.19734/j.issn.1001-3695.2019.09.0531

Abstract

Aiming at the problem of the discriminator's poor classification accuracy on a small number of labeled samples and insufficient robustness to the local perturbation of manifolds in the generative adversarial network, this paper proposed a gene-rative adversarial network based on variable loss and manifold regularization. The algorithm used a variable loss instead of the original discriminator to solve the adverse effect of the poorly trained classifier on the semi-supervised classification task. In addition, on the basis of variable loss of discriminator, it added manifold regular terms to improve the robustness of discriminator to local disturbance by punishing the variation of classification decision of discriminator on manifold. Using the quality of the generated samples and the semi-supervised classification accuracy as the evaluation criteria of the algorithm, it performed numerical experiments on the dataset SVHN and CIFAR-10. Comparing with other semi-supervised algorithms, the results show that the proposed algorithm can obtain higher quality generated samples and higher precision classification results with a small amount of labeled data.

Foundation Support

重庆市自然科学基金资助项目(cstc2014jcyjA40011)
重庆师范大学科研项目(YKC19018)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.09.0531
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 12
Section: Algorithm Research & Explore
Pages: 3607-3611
Serial Number: 1001-3695(2020)12-018-3607-05

Publish History

[2020-12-05] Printed Article

Cite This Article

丁赛赛, 吕佳. 基于可变损失和流形正则化的生成对抗网络 [J]. 计算机应用研究, 2020, 37 (12): 3607-3611. (Ding Saisai, Lyu Jia. GAN based on variable loss and manifold regularization [J]. Application Research of Computers, 2020, 37 (12): 3607-3611. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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