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