Research on the Style of Art Works based on Deep Learning
In view of the unsatisfactory effect and major limitations of the style transfer of art works, this paper takes Chinese ink painting for the research subject. The obvious texture characteristics of Chinese ink painting are selected as the input of the Cycle Generative Adversarial Network (CycleGAN)...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/5433623 |
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| _version_ | 1849693426491064320 |
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| author | Shulin Liu |
| author_facet | Shulin Liu |
| author_sort | Shulin Liu |
| collection | DOAJ |
| description | In view of the unsatisfactory effect and major limitations of the style transfer of art works, this paper takes Chinese ink painting for the research subject. The obvious texture characteristics of Chinese ink painting are selected as the input of the Cycle Generative Adversarial Network (CycleGAN) model builder, and the relativistic evaluator is employed to improve the model loss function and the adversarial loss function. An improved art style transfer method of the CycleGAN model is proposed. The experiment shows that the improved CycleGAN model is efficient and feasible for style transfer. Compared with the traditional CycleGAN model, the proposed model performs better in GAN train and GAN test, with a higher average pass rate, which is an increase of nearly 10%. At the same time, with the increase of the number of iterations, the training time of the improved model is close to that of the original model, but the image of the improved model training is larger, which shows that it has more advantages. |
| format | Article |
| id | doaj-art-2c820d1924b44f40ab2d2fe84d112477 |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2c820d1924b44f40ab2d2fe84d1124772025-08-20T03:20:25ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5433623Research on the Style of Art Works based on Deep LearningShulin Liu0Department of Fine ArtsIn view of the unsatisfactory effect and major limitations of the style transfer of art works, this paper takes Chinese ink painting for the research subject. The obvious texture characteristics of Chinese ink painting are selected as the input of the Cycle Generative Adversarial Network (CycleGAN) model builder, and the relativistic evaluator is employed to improve the model loss function and the adversarial loss function. An improved art style transfer method of the CycleGAN model is proposed. The experiment shows that the improved CycleGAN model is efficient and feasible for style transfer. Compared with the traditional CycleGAN model, the proposed model performs better in GAN train and GAN test, with a higher average pass rate, which is an increase of nearly 10%. At the same time, with the increase of the number of iterations, the training time of the improved model is close to that of the original model, but the image of the improved model training is larger, which shows that it has more advantages.http://dx.doi.org/10.1155/2022/5433623 |
| spellingShingle | Shulin Liu Research on the Style of Art Works based on Deep Learning Journal of Advanced Transportation |
| title | Research on the Style of Art Works based on Deep Learning |
| title_full | Research on the Style of Art Works based on Deep Learning |
| title_fullStr | Research on the Style of Art Works based on Deep Learning |
| title_full_unstemmed | Research on the Style of Art Works based on Deep Learning |
| title_short | Research on the Style of Art Works based on Deep Learning |
| title_sort | research on the style of art works based on deep learning |
| url | http://dx.doi.org/10.1155/2022/5433623 |
| work_keys_str_mv | AT shulinliu researchonthestyleofartworksbasedondeeplearning |