Deep Generative Image Models using a ´┐╝Laplacian Pyramid of Adversarial Networks

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

Bibtex Metadata Paper Reviews Supplemental

Authors

Emily L. Denton, Soumith Chintala, arthur szlam, Rob Fergus

Abstract

In this paper we introduce a generative model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks (convnets) within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach. Samples drawn from our model are of significantly higher quality than existing models. In a quantitive assessment by human evaluators our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for GAN samples. We also show samples from more diverse datasets such as STL10 and LSUN.