InstaGAN Excels in Instance-Aware Image-To-Image Translation

Researchers at the Korea Advanced Institute of Science and Technology and Pohang University of Science and Technology have introduced a machine learning algorithm system, InstaGAN, which can perform multiple instance-aware image-to-image translation tasks -- such as replacing sheep in photos with giraffes -- on multiple image datasets. The paper InstaGAN: Instance-Aware Image-to-Image Translation has been accepted by the respected International Conference on Learning Representations (ICLR) 2019, which will take place this May in New Orleans, USA. An image-to-image translation system is a system that learns to map an input image onto an output image. Unsupervised image-to-image translation has garnered considerable research attention recently in part due to the rapid development of generative adversarial networks (GANs) that now power the technique. Previous methods were not suitable for challenging tasks, for example if the image has multiple target instances or if the translation task involves challenging shapes.