{"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/tensorizing-generative-adversarial-nets","title":"Tensorizing Generative Adversarial Nets","arxiv_id":"1710.10772","date":"2017-10-30","proceeding":null,"authors":["Xingwei Cao","Xuyang Zhao","Qibin Zhao"],"abstract":"Generative Adversarial Network (GAN) and its variants exhibit\nstate-of-the-art performance in the class of generative models. To capture\nhigher-dimensional distributions, the common learning procedure requires high\ncomputational complexity and a large number of parameters. The problem of\nemploying such massive framework arises when deploying it on a platform with\nlimited computational power such as mobile phones. In this paper, we present a\nnew generative adversarial framework by representing each layer as a tensor\nstructure connected by multilinear operations, aiming to reduce the number of\nmodel parameters by a large factor while preserving the generative performance\nand sample quality. To learn the model, we employ an efficient algorithm which\nalternatively optimizes both discriminator and generator. Experimental outcomes\ndemonstrate that our model can achieve high compression rate for model\nparameters up to $35$ times when compared to the original GAN for MNIST\ndataset.","url_abs":"http://arxiv.org/abs/1710.10772v2","url_pdf":"http://arxiv.org/pdf/1710.10772v2.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":"tensorizing-generative-adversarial-nets","repo_url":"https://github.com/xwcao/TGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}