A progressive growing of conditional generative adversarial networks model
Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar, it is prone to produce mode collapse, result...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023134/ |
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| author | Hui MA Ruiqin WANG Shuai YANG |
| author_facet | Hui MA Ruiqin WANG Shuai YANG |
| author_sort | Hui MA |
| collection | DOAJ |
| description | Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar, it is prone to produce mode collapse, resulting in poor image generation effect.A progressive growing of conditional generative adversarial networks (PGCGAN) model was proposed.The idea of conditional generative adversarial networks (CGAN) was introduced into PGGAN.Using category information as condition, PGGAN was improved in two aspects of network structure and mini-batch standard deviation, and the phenomenon of model collapse in the process of image generation was alleviated.In the experiments on the three data sets, compared with PGGAN, PGCGAN has a greater degree of improvement in inception score and Fréchet inception distance, two evaluation indicators for image generation, and the generated images have higher diversity and authenticity; and PGCGAN multiple unrelated datasets can be trained simultaneously without crashing, and high-quality images can be produced in datasets with imbalanced categories or data that are too similar and dissimilar. |
| format | Article |
| id | doaj-art-8007af84f24a47ff86bb2a816cd899b6 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-8007af84f24a47ff86bb2a816cd899b62025-01-15T02:58:34ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-06-013910511359565935A progressive growing of conditional generative adversarial networks modelHui MARuiqin WANGShuai YANGProgressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar, it is prone to produce mode collapse, resulting in poor image generation effect.A progressive growing of conditional generative adversarial networks (PGCGAN) model was proposed.The idea of conditional generative adversarial networks (CGAN) was introduced into PGGAN.Using category information as condition, PGGAN was improved in two aspects of network structure and mini-batch standard deviation, and the phenomenon of model collapse in the process of image generation was alleviated.In the experiments on the three data sets, compared with PGGAN, PGCGAN has a greater degree of improvement in inception score and Fréchet inception distance, two evaluation indicators for image generation, and the generated images have higher diversity and authenticity; and PGCGAN multiple unrelated datasets can be trained simultaneously without crashing, and high-quality images can be produced in datasets with imbalanced categories or data that are too similar and dissimilar.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023134/generative adversarial networkprogressive growing of conditional GANmini-batch standard deviationimage generation |
| spellingShingle | Hui MA Ruiqin WANG Shuai YANG A progressive growing of conditional generative adversarial networks model Dianxin kexue generative adversarial network progressive growing of conditional GAN mini-batch standard deviation image generation |
| title | A progressive growing of conditional generative adversarial networks model |
| title_full | A progressive growing of conditional generative adversarial networks model |
| title_fullStr | A progressive growing of conditional generative adversarial networks model |
| title_full_unstemmed | A progressive growing of conditional generative adversarial networks model |
| title_short | A progressive growing of conditional generative adversarial networks model |
| title_sort | progressive growing of conditional generative adversarial networks model |
| topic | generative adversarial network progressive growing of conditional GAN mini-batch standard deviation image generation |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023134/ |
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