| Autor: |
SHEN Ruicai, ZHAI Junhai, HOU Yingzhen |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Jisuanji kexue yu tansuo, Vol 16, Iss 6, Pp 1429-1438 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1673-9418 |
| DOI: |
10.3778/j.issn.1673-9418.2011010 |
| Popis: |
Generative adversarial networks (GAN) are widely used in image generation. However, there is still a big gap between the samples generated by unsupervised and supervised networks. In order to solve the problems such as poor diversity, low quality and long training time of GAN in unsupervised environment, a new model with selective ensemble learning is proposed. Specifically, the discriminator in GAN is adopted in the form of integrated discrimination system, which can effectively reduce the discrimination error caused by the poor performance of single discriminator. Considering that if the integrated discriminant networks are set up in a unified network, each base discriminant network will tend to a form of expression in the model training. In order to encourage the diversity of discriminant network results and avoid the network falling into the same one, the discriminant networks with different network structures are set up. The majority voting strategy with dynamically adjusting the voting weight of the base discriminant network is introduced to vote the results of the integrated discriminant network. This has been shown to be effective in promoting model convergence and reducing experimental error significantly. Finally, the proposed model and the models in the same direction are evaluated with different evaluation indices under different datasets. Experimental results show that the proposed model is superior to several competitive models in terms of the diversity of generated samples, the quality of generated samples and the convergence speed of the model. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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