{"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/energy-based-generative-adversarial-network","title":"Energy-based Generative Adversarial Network","arxiv_id":"1609.03126","date":"2016-09-11","proceeding":null,"authors":["Junbo Zhao","Michael Mathieu","Yann Lecun"],"abstract":"We introduce the \"Energy-based Generative Adversarial Network\" model (EBGAN)\nwhich views the discriminator as an energy function that attributes low\nenergies to the regions near the data manifold and higher energies to other\nregions. Similar to the probabilistic GANs, a generator is seen as being\ntrained to produce contrastive samples with minimal energies, while the\ndiscriminator is trained to assign high energies to these generated samples.\nViewing the discriminator as an energy function allows to use a wide variety of\narchitectures and loss functionals in addition to the usual binary classifier\nwith logistic output. Among them, we show one instantiation of EBGAN framework\nas using an auto-encoder architecture, with the energy being the reconstruction\nerror, in place of the discriminator. We show that this form of EBGAN exhibits\nmore stable behavior than regular GANs during training. We also show that a\nsingle-scale architecture can be trained to generate high-resolution images.","url_abs":"http://arxiv.org/abs/1609.03126v4","url_pdf":"http://arxiv.org/pdf/1609.03126v4.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":"energy-based-generative-adversarial-network","repo_url":"https://github.com/buriburisuri/ebgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"energy-based-generative-adversarial-network","repo_url":"https://github.com/eriklindernoren/PyTorch-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"energy-based-generative-adversarial-network","repo_url":"https://github.com/evan11401/CS_IOC5008_0856043_HW2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.03126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.03126"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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