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

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

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Authors

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

Abstract

<p>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.</p>