{"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/unsupervised-representation-adversarial","title":"Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation","arxiv_id":"1804.07353","date":"2018-04-19","proceeding":null,"authors":["Yuqian Zhou","Kuangxiao Gu","Thomas Huang"],"abstract":"A good representation for arbitrarily complicated data should have the\ncapability of semantic generation, clustering and reconstruction. Previous\nresearch has already achieved impressive performance on either one. This paper\naims at learning a disentangled representation effective for all of them in an\nunsupervised way. To achieve all the three tasks together, we learn the forward\nand inverse mapping between data and representation on the basis of a symmetric\nadversarial process. In theory, we minimize the upper bound of the two\nconditional entropy loss between the latent variables and the observations\ntogether to achieve the cycle consistency. The newly proposed RepGAN is tested\non MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised\nclassification, generation and reconstruction tasks. The result demonstrates\nthat RepGAN is able to learn a useful and competitive representation. To the\nauthor's knowledge, our work is the first one to achieve both a high\nunsupervised classification accuracy and low reconstruction error on MNIST.\nCodes are available at https://github.com/yzhouas/RepGAN-tensorflow.","url_abs":"http://arxiv.org/abs/1804.07353v2","url_pdf":"http://arxiv.org/pdf/1804.07353v2.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":"unsupervised-representation-adversarial","repo_url":"https://github.com/yzhouas/RepGAN-tensorflow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.07353","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}