{"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/unseen-class-discovery-in-open-world","title":"Unseen Class Discovery in Open-world Classification","arxiv_id":"1801.05609","date":"2018-01-17","proceeding":"ICLR 2018 1","authors":["Lei Shu","Hu Xu","Bing Liu"],"abstract":"This paper concerns open-world classification, where the classifier not only\nneeds to classify test examples into seen classes that have appeared in\ntraining but also reject examples from unseen or novel classes that have not\nappeared in training. Specifically, this paper focuses on discovering the\nhidden unseen classes of the rejected examples. Clearly, without prior\nknowledge this is difficult. However, we do have the data from the seen\ntraining classes, which can tell us what kind of similarity/difference is\nexpected for examples from the same class or from different classes. It is\nreasonable to assume that this knowledge can be transferred to the rejected\nexamples and used to discover the hidden unseen classes in them. This paper\naims to solve this problem. It first proposes a joint open classification model\nwith a sub-model for classifying whether a pair of examples belongs to the same\nor different classes. This sub-model can serve as a distance function for\nclustering to discover the hidden classes of the rejected examples.\nExperimental results show that the proposed model is highly promising.","url_abs":"http://arxiv.org/abs/1801.05609v1","url_pdf":"http://arxiv.org/pdf/1801.05609v1.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":"unseen-class-discovery-in-open-world","repo_url":"https://github.com/leishu02/EMNLP2017_DOC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1801.05609","atlas_url":"https://app.syntology.ai/?focus=1801.05609","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}