Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Emily Denton; Soumith Chintala; Arthur Szlam; Rob Fergus

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arXiv · 1506.05751

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Emily Denton·Soumith Chintala·Arthur Szlam·Rob Fergus

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Abstract

In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks 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 (Goodfellow et al.). Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset.

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BibTeX RIS

Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus. 2015-06-18. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. https://arxiv.org/abs/1506.05751

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