@inproceedings{5391f444e3344877b7e3e45754f8a505,
title = "Unsupervised Image-to-Image Translation Based on Bidirectional Style Transfer",
abstract = "Image-to-image translation (I2I) is an image synthesis technique to map a source image to the style of the target domain while preserving its content information. Existing image-to-image translation study results showed excellent image synthesis performance using generative adversarial network (GAN) based models, but they are not capable of efficiently handling the style of the target domain. To overcome this limitation, a bidirectional style transfer network has been developed to perform image synthesis by intersecting images of two domains with each other's styles, but the type of applicable dataset is limited due to supervised learning-based training. We proposed an unsupervised image-to-image translation method by employing a bidirectional style transfer network with a cyclic collaborative loss to train the model. Experimental results showed that the proposed network accurately reflected the style of the target domain in the image synthesis task.",
keywords = "bidirectional network, style transfer, unsupervised image-to-image translation (I2I)",
author = "Hyunkyu Park and Sungho Kang and Park, \{Yeong Hyeon\} and Yeonho Lee and Hanbyul Lee and Seho Bae and Juneho Yi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023 ; Conference date: 11-08-2023 Through 13-08-2023",
year = "2023",
doi = "10.1109/ICKII58656.2023.10332712",
language = "English",
series = "Proceedings of the 2023 IEEE 6th International Conference on Knowledge Innovation and Invention, ICKII 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "671--676",
editor = "Teen-Hang Meen",
booktitle = "Proceedings of the 2023 IEEE 6th International Conference on Knowledge Innovation and Invention, ICKII 2023",
}