{"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/adversarial-data-programming-using-gans-to","title":"Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data","arxiv_id":"1803.05137","date":"2018-03-14","proceeding":"CVPR 2018 6","authors":["Arghya Pal","Vineeth N. Balasubramanian"],"abstract":"Paucity of large curated hand-labeled training data for every\ndomain-of-interest forms a major bottleneck in the deployment of machine\nlearning models in computer vision and other fields. Recent work (Data\nProgramming) has shown how distant supervision signals in the form of labeling\nfunctions can be used to obtain labels for given data in near-constant time. In\nthis work, we present Adversarial Data Programming (ADP), which presents an\nadversarial methodology to generate data as well as a curated aggregated label\nhas given a set of weak labeling functions. We validated our method on the\nMNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many\nstate-of-the-art models. We conducted extensive experiments to study its\nusefulness, as well as showed how the proposed ADP framework can be used for\ntransfer learning as well as multi-task learning, where data from two domains\nare generated simultaneously using the framework along with the label\ninformation. Our future work will involve understanding the theoretical\nimplications of this new framework from a game-theoretic perspective, as well\nas explore the performance of the method on more complex datasets.","url_abs":"http://arxiv.org/abs/1803.05137v1","url_pdf":"http://arxiv.org/pdf/1803.05137v1.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":"adversarial-data-programming-using-gans-to","repo_url":"https://github.com/ArghyaPal/Adversarial-Data-Programming","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.05137","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}