{"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/improving-gan-training-via-binarized","title":"Improving GAN Training via Binarized Representation Entropy (BRE) Regularization","arxiv_id":"1805.03644","date":"2018-05-09","proceeding":"ICLR 2018 1","authors":["Yanshuai Cao","Gavin Weiguang Ding","Kry Yik-Chau Lui","Ruitong Huang"],"abstract":"We propose a novel regularizer to improve the training of Generative\nAdversarial Networks (GANs). The motivation is that when the discriminator D\nspreads out its model capacity in the right way, the learning signals given to\nthe generator G are more informative and diverse. These in turn help G to\nexplore better and discover the real data manifold while avoiding large\nunstable jumps due to the erroneous extrapolation made by D. Our regularizer\nguides the rectifier discriminator D to better allocate its model capacity, by\nencouraging the binary activation patterns on selected internal layers of D to\nhave a high joint entropy. Experimental results on both synthetic data and real\ndatasets demonstrate improvements in stability and convergence speed of the GAN\ntraining, as well as higher sample quality. The approach also leads to higher\nclassification accuracies in semi-supervised learning.","url_abs":"http://arxiv.org/abs/1805.03644v1","url_pdf":"http://arxiv.org/pdf/1805.03644v1.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":"improving-gan-training-via-binarized","repo_url":"https://github.com/BorealisAI/bre-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}