Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning
Joint Authors
Yang, Wenjia
Bi, Fangming
Man, Zijian
Xia, Yang
Liu, Wei
Fu, Xuanyi
Gao, Lei
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Generative adversarial networks are currently used to solve various problems and are one of the most popular models.
Generator and discriminator are characteristics of continuous game process in training.
While improving the quality of generated pictures, it will also make it difficult for the loss function to be stable, and the training speed will be extremely slow compared with other methods.
In addition, since the generative adversarial networks directly learns the data distribution of samples, the model will become uncontrollable and the freedom of the model will become too large when the original data distribution is constantly approximated.
A new transfer learning training idea for the unsupervised generation model is proposed based on the generation network.
The decoder of trained variational autoencoders is used as the network architecture and parameters to generative adversarial network generator.
In addition, the standard normal distribution is obtained by sampling and then input into the model to control the degree of freedom of the model.
Finally, we evaluated our method on using the MNIST, CIFAR10, and LSUN datasets.
The experiment shows that our proposed method can make the loss function converge as quickly as possible and increase the model accuracy.
American Psychological Association (APA)
Bi, Fangming& Man, Zijian& Xia, Yang& Liu, Wei& Yang, Wenjia& Fu, Xuanyi…[et al.]. 2020. Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202242
Modern Language Association (MLA)
Bi, Fangming…[et al.]. Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1202242
American Medical Association (AMA)
Bi, Fangming& Man, Zijian& Xia, Yang& Liu, Wei& Yang, Wenjia& Fu, Xuanyi…[et al.]. Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202242
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202242