{"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/a-study-into-the-similarity-in-generator-and","title":"A Study into the similarity in generator and discriminator in GAN architecture","arxiv_id":"1802.07401","date":"2018-02-21","proceeding":null,"authors":["Arjun Karuvally"],"abstract":"One popular generative model that has high-quality results is the Generative\nAdversarial Networks(GAN). This type of architecture consists of two separate\nnetworks that play against each other. The generator creates an output from the\ninput noise that is given to it. The discriminator has the task of determining\nif the input to it is real or fake. This takes place constantly eventually\nleads to the generator modeling the target distribution. This paper includes a\nstudy into the actual weights learned by the network and a study into the\nsimilarity of the discriminator and generator networks. The paper also tries to\nleverage the similarity between these networks and shows that indeed both the\nnetworks may have a similar structure with experimental evidence with a novel\nshared architecture.","url_abs":"http://arxiv.org/abs/1802.07401v1","url_pdf":"http://arxiv.org/pdf/1802.07401v1.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":"a-study-into-the-similarity-in-generator-and","repo_url":"https://github.com/arjun23496/Shared-WGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}