{"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/open-world-learning-and-application-to","title":"Open-world Learning and Application to Product Classification","arxiv_id":"1809.06004","date":"2018-09-17","proceeding":null,"authors":["Hu Xu","Bing Liu","Lei Shu","P. Yu"],"abstract":"Classic supervised learning makes the closed-world assumption, meaning that\nclasses seen in testing must have been seen in training. However, in the\ndynamic world, new or unseen class examples may appear constantly. A model\nworking in such an environment must be able to reject unseen classes (not seen\nor used in training). If enough data is collected for the unseen classes, the\nsystem should incrementally learn to accept/classify them. This learning\nparadigm is called open-world learning (OWL). Existing OWL methods all need\nsome form of re-training to accept or include the new classes in the overall\nmodel. In this paper, we propose a meta-learning approach to the problem. Its\nkey novelty is that it only needs to train a meta-classifier, which can then\ncontinually accept new classes when they have enough labeled data for the\nmeta-classifier to use, and also detect/reject future unseen classes. No\nre-training of the meta-classifier or a new overall classifier covering all old\nand new classes is needed. In testing, the method only uses the examples of the\nseen classes (including the newly added classes) on-the-fly for classification\nand rejection. Experimental results demonstrate the effectiveness of the new\napproach.","url_abs":"http://arxiv.org/abs/1809.06004v2","url_pdf":"http://arxiv.org/pdf/1809.06004v2.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":"open-world-learning-and-application-to","repo_url":"https://github.com/howardhsu/Meta-Open-World-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.06004","atlas_url":"https://app.syntology.ai/?focus=1809.06004","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}