Fabbri, CameronSattar, Junaed2020-09-022020-09-022018-07-09https://hdl.handle.net/11299/216028This report describes various applications of Generative Adversarial Networks (GANs) for image generation, image-to-image translation, and vehicle control. With this, we also investigate the role played by the computed latent space, and show various ways of exploiting this space for controlled image generation and exploration. We show one pure generative method which we call AstroGAN that is able to generate realistic images of galaxies from a set of galaxy morphologies. Two image-to-image translation methods are also displayed: StereoGAN, which is able to generate a pair of stereo images given a single image; Underwater GAN, which is able to restore distorted imagery exhibited in underwater environments. Lastly, we show a generative model for generating actions in a simulated self-driving car environment.en-USReport