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Poster
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Emily Denton · Soumith Chintala · arthur szlam · Rob Fergus

Tue Dec 08 04:00 PM -- 08:59 PM (PST) @ 210 C #1 #None
in Poster Session »

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.

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Author Information

Emily Denton (New York University)
Soumith Chintala (Facebook AI Research)
arthur szlam (Facebook)
Rob Fergus (Facebook AI Research)

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