Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks
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.
Cite
Text
Denton et al. "Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks." Neural Information Processing Systems, 2015.Markdown
[Denton et al. "Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/denton2015neurips-deep/)BibTeX
@inproceedings{denton2015neurips-deep,
title = {{Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks}},
author = {Denton, Emily L and Chintala, Soumith and Szlam, Arthur and Fergus, Rob},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {1486-1494},
url = {https://mlanthology.org/neurips/2015/denton2015neurips-deep/}
}