Nicolas Cruz

Generation of Realistic Images for Learning in Simulation using FeatureGAN

This paper presents FeatureGan, a methodology to train image translators (generators) using an unpaired image training set. FeatureGan is based on the use of Generative Adversarial Networks (GAN) and has three main novel components: (i) the use of a feature loss to ensure alignment between the input