Engineering Commons^™

Inverse Mapping Of Generative Adversarial Networks, Nicky Bayat

Electronic Thesis and Dissertation Repository

Generative adversarial networks (GANs) synthesize realistic samples (image, audio, video, etc.) from a random latent vector. While many studies have explored various training configurations and architectures for GANs, the problem of inverting a generative model to extract latent vectors of given input images/audio has been inadequately investigated. Although there is exactly one generated output per given random vector, the mapping from an image/audio to its recovered latent vector can have more than one solution. We train a deep residual neural network (ResNet18) architecture to recover a latent vector for a given target that can be used to generate a face …