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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
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)

Emily Denton is a Research Scientist at Google where they examine the societal impacts of AI technology. Their recent research centers on critically examining the norms, values, and work practices that structure the development and use of machine learning datasets. Prior to joining Google, Emily received their PhD in machine learning from the Courant Institute of Mathematical Sciences at New York University, where they focused on unsupervised learning and generative modeling of images and video.

Soumith Chintala (Facebook AI Research)
arthur szlam (Facebook)
Rob Fergus (Facebook AI Research)

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