CoachGAN

Mike Brodie; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3483-3492

CoachGAN provides an inference time method to improve outputs from GAN generator models. Similar to creating adversarial examples to fool neural network classifiers, CoachGAN exploits gradient information, in this case from a pretrained discriminator model. Unlike the process of generating adversarial examples, which uses gradient descent to alter outputs directly, CoachGAN alters the inputs of generator models. This allows for output enhancements at test time without any additional model training. CoachGAN adapts easily to existing algorithms and does not depend on specific model architectures. In addition to qualitative samples, we quantitatively demonstrate the ability of CoachGAN to improve IS and FID scores across a variety of GAN architectures and tasks.