{"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/rankgan-a-maximum-margin-ranking-gan-for","title":"RankGAN: A Maximum Margin Ranking GAN for Generating Faces","arxiv_id":"1812.08196","date":"2018-12-19","proceeding":null,"authors":["Rahul Dey","Felix Juefei-Xu","Vishnu Naresh Boddeti","Marios Savvides"],"abstract":"We present a new stage-wise learning paradigm for training generative\nadversarial networks (GANs). The goal of our work is to progressively\nstrengthen the discriminator and thus, the generators, with each subsequent\nstage without changing the network architecture. We call this proposed method\nthe RankGAN. We first propose a margin-based loss for the GAN discriminator. We\nthen extend it to a margin-based ranking loss to train the multiple stages of\nRankGAN. We focus on face images from the CelebA dataset in our work and show\nvisual as well as quantitative improvements in face generation and completion\ntasks over other GAN approaches, including WGAN and LSGAN.","url_abs":"http://arxiv.org/abs/1812.08196v1","url_pdf":"http://arxiv.org/pdf/1812.08196v1.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":"rankgan-a-maximum-margin-ranking-gan-for","repo_url":"https://github.com/human-analysis/RankGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"face-generation","task_name":"Face Generation"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"gan-least-squares-loss","method_name":"GAN Least Squares Loss"},{"method_slug":"lsgan","method_name":"LSGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"wgan","method_name":"WGAN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}