StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this li...
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| Vydáno v: | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 8789 - 8797 |
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| Jazyk: | angličtina |
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IEEE
01.06.2018
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| ISSN: | 1063-6919 |
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| Abstract | Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks. |
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| AbstractList | Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks. |
| Author | Choi, Yunjey Choi, Minje Kim, Sunghun Ha, Jung-Woo Choo, Jaegul Kim, Munyoung |
| Author_xml | – sequence: 1 givenname: Yunjey surname: Choi fullname: Choi, Yunjey – sequence: 2 givenname: Minje surname: Choi fullname: Choi, Minje – sequence: 3 givenname: Munyoung surname: Kim fullname: Kim, Munyoung – sequence: 4 givenname: Jung-Woo surname: Ha fullname: Ha, Jung-Woo – sequence: 5 givenname: Sunghun surname: Kim fullname: Kim, Sunghun – sequence: 6 givenname: Jaegul surname: Choo fullname: Choo, Jaegul |
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| Snippet | Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and... |
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| SubjectTerms | Gallium nitride Generative adversarial networks Generators Hair Image reconstruction Task analysis Training |
| Title | StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation |
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