{"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/generative-adversarial-networks-an-overview","title":"Generative Adversarial Networks: An Overview","arxiv_id":"1710.07035","date":"2017-10-19","proceeding":null,"authors":["Antonia Creswell","Tom White","Vincent Dumoulin","Kai Arulkumaran","Biswa Sengupta","Anil A. Bharath"],"abstract":"Generative adversarial networks (GANs) provide a way to learn deep\nrepresentations without extensively annotated training data. They achieve this\nthrough deriving backpropagation signals through a competitive process\ninvolving a pair of networks. The representations that can be learned by GANs\nmay be used in a variety of applications, including image synthesis, semantic\nimage editing, style transfer, image super-resolution and classification. The\naim of this review paper is to provide an overview of GANs for the signal\nprocessing community, drawing on familiar analogies and concepts where\npossible. In addition to identifying different methods for training and\nconstructing GANs, we also point to remaining challenges in their theory and\napplication.","url_abs":"http://arxiv.org/abs/1710.07035v1","url_pdf":"http://arxiv.org/pdf/1710.07035v1.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":"generative-adversarial-networks-an-overview","repo_url":"https://github.com/NicicDamjan/SoftKompjuting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"generative-adversarial-networks-an-overview","repo_url":"https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generative-adversarial-networks-an-overview","repo_url":"https://github.com/mshaikh2/GANs_Comparison","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.07035","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}