Unusual Transformation: A Deep Learning Approach to Create Art
In this research, we proposed the concept of “unusual transformation,” which realizes transformation between two very different image sets as an extension of CycleGAN. CycleGAN is a new deep-learning-based AI technology that can realize transformation between two image sets. Although conventional Cy...
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| Định dạng: | Bài trích |
| Ngôn ngữ: | English |
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Springer
2022
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| Truy cập trực tuyến: | https://link.springer.com/chapter/10.1007/978-3-030-95531-1_21 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5871 https://doi.org/10.1007/978-3-030-95531-1_21 |
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oai:localhost:PNK-58712022-08-17T05:54:54Z Unusual Transformation: A Deep Learning Approach to Create Art Mai, Cong Hung Mai, Xuan Trang Ryohei, Nakatsu Naoko, Tosa GANs CycleGAN In this research, we proposed the concept of “unusual transformation,” which realizes transformation between two very different image sets as an extension of CycleGAN. CycleGAN is a new deep-learning-based AI technology that can realize transformation between two image sets. Although conventional CycleGAN researchers have tried transformation between two similar image sets, we applied CycleGAN to the transformation of two very different image sets such as between portraits photos and Ikebana or Shan-Shui paintings. Then to obtain a better result, we improved CycleGAN by adding a new loss function and developed “UTGAN (Unusual Transformation GAN).” We found that by using UTGAN, portrait photos and animal photos are transformed into Ikabana-like and Shan-Shui-like images. Then we carried out an analysis of the obtained result and made a hypothesis that the unusual transformation works well because both Ikebana and Shan-Shui are fundamental and abstracted expressions of nature. Also, we carried out various considerations to justify the hypothesis. 2022-07-13T01:59:46Z 2022-07-13T01:59:46Z 2022 Bài trích https://link.springer.com/chapter/10.1007/978-3-030-95531-1_21 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5871 https://doi.org/10.1007/978-3-030-95531-1_21 en Springer |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
GANs CycleGAN |
| spellingShingle |
GANs CycleGAN Mai, Cong Hung Mai, Xuan Trang Ryohei, Nakatsu Naoko, Tosa Unusual Transformation: A Deep Learning Approach to Create Art |
| description |
In this research, we proposed the concept of “unusual transformation,” which realizes transformation between two very different image sets as an extension of CycleGAN. CycleGAN is a new deep-learning-based AI technology that can realize transformation between two image sets. Although conventional CycleGAN researchers have tried transformation between two similar image sets, we applied CycleGAN to the transformation of two very different image sets such as between portraits photos and Ikebana or Shan-Shui paintings. Then to obtain a better result, we improved CycleGAN by adding a new loss function and developed “UTGAN (Unusual Transformation GAN).” We found that by using UTGAN, portrait photos and animal photos are transformed into Ikabana-like and Shan-Shui-like images. Then we carried out an analysis of the obtained result and made a hypothesis that the unusual transformation works well because both Ikebana and Shan-Shui are fundamental and abstracted expressions of nature. Also, we carried out various considerations to justify the hypothesis. |
| format |
Bài trích |
| author |
Mai, Cong Hung Mai, Xuan Trang Ryohei, Nakatsu Naoko, Tosa |
| author_facet |
Mai, Cong Hung Mai, Xuan Trang Ryohei, Nakatsu Naoko, Tosa |
| author_sort |
Mai, Cong Hung |
| title |
Unusual Transformation: A Deep Learning Approach to Create Art |
| title_short |
Unusual Transformation: A Deep Learning Approach to Create Art |
| title_full |
Unusual Transformation: A Deep Learning Approach to Create Art |
| title_fullStr |
Unusual Transformation: A Deep Learning Approach to Create Art |
| title_full_unstemmed |
Unusual Transformation: A Deep Learning Approach to Create Art |
| title_sort |
unusual transformation: a deep learning approach to create art |
| publisher |
Springer |
| publishDate |
2022 |
| url |
https://link.springer.com/chapter/10.1007/978-3-030-95531-1_21 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5871 https://doi.org/10.1007/978-3-030-95531-1_21 |
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1751856285401219072 |
| score |
8.891145 |