GANs with Multiple Constraints for Image Translation
Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which result...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4613935 |
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| _version_ | 1849413349615337472 |
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| author | Yan Gan Junxin Gong Mao Ye Yang Qian Kedi Liu Su Zhang |
| author_facet | Yan Gan Junxin Gong Mao Ye Yang Qian Kedi Liu Su Zhang |
| author_sort | Yan Gan |
| collection | DOAJ |
| description | Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs-based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model. |
| format | Article |
| id | doaj-art-678e034d1acb4ac3ad938283626876ab |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-678e034d1acb4ac3ad938283626876ab2025-08-20T03:34:09ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/46139354613935GANs with Multiple Constraints for Image TranslationYan Gan0Junxin Gong1Mao Ye2Yang Qian3Kedi Liu4Su Zhang5School of Computer Science and Engineering, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaUnpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs-based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model.http://dx.doi.org/10.1155/2018/4613935 |
| spellingShingle | Yan Gan Junxin Gong Mao Ye Yang Qian Kedi Liu Su Zhang GANs with Multiple Constraints for Image Translation Complexity |
| title | GANs with Multiple Constraints for Image Translation |
| title_full | GANs with Multiple Constraints for Image Translation |
| title_fullStr | GANs with Multiple Constraints for Image Translation |
| title_full_unstemmed | GANs with Multiple Constraints for Image Translation |
| title_short | GANs with Multiple Constraints for Image Translation |
| title_sort | gans with multiple constraints for image translation |
| url | http://dx.doi.org/10.1155/2018/4613935 |
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