StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

CVPR 2018 Yunjey Choi • Minje Choi • Munyoung Kim • Jung-Woo Ha • Sunghun Kim • Jaegul Choo

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.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Image-to-Image Translation RaFD StarGAN Classification Error 2.12% # 1