{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-generative-image-models-using-a-1","title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","arxiv_id":"1506.05751","date":"2015-06-18","proceeding":null,"authors":["Emily Denton","Soumith Chintala","Arthur Szlam","Rob Fergus"],"abstract":"In this paper we introduce a generative parametric model capable of producing\nhigh quality samples of natural images. Our approach uses a cascade of\nconvolutional networks within a Laplacian pyramid framework to generate images\nin a coarse-to-fine fashion. At each level of the pyramid, a separate\ngenerative convnet model is trained using the Generative Adversarial Nets (GAN)\napproach (Goodfellow et al.). Samples drawn from our model are of significantly\nhigher quality than alternate approaches. In a quantitative assessment by human\nevaluators, our CIFAR10 samples were mistaken for real images around 40% of the\ntime, compared to 10% for samples drawn from a GAN baseline model. We also show\nsamples from models trained on the higher resolution images of the LSUN scene\ndataset.","url_abs":"http://arxiv.org/abs/1506.05751v1","url_pdf":"http://arxiv.org/pdf/1506.05751v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-generative-image-models-using-a-1","repo_url":"https://github.com/facebook/eyescream","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"lapgan","method_name":"LAPGAN"},{"method_slug":"laplacian-pyramid","method_name":"Laplacian Pyramid"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sgd-with-momentum","method_name":"SGD with Momentum"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[{"slug":"lapgan","name":"LAPGAN","full_name":"LAPGAN"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.05751","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}