Neural Photo Editing With Introspective Adversarial Networks

Abstract

We present the Neural Photo Editor, an interface for exploring the latent space of generative image models and making large, semantically coherent changes to existing images. Our interface is powered by the Introspective Adversarial Network, a hybridization of the Generative Adversarial Network and the Variational Autoencoder designed for use in the editor. Our model makes use of a novel computational block based on dilated convolutions, and Orthogonal Regularization, a novel weight regularization method. We validate our model on CelebA, SVHN, and ImageNet, and produce samples and reconstructions with high visual fidelity.