{"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/learning-neural-models-for-end-to-end","title":"Learning Neural Models for End-to-End Clustering","arxiv_id":"1807.04001","date":"2018-07-11","proceeding":null,"authors":["Benjamin Bruno Meier","Ismail Elezi","Mohammadreza Amirian","Oliver Durr","Thilo Stadelmann"],"abstract":"We propose a novel end-to-end neural network architecture that, once trained,\ndirectly outputs a probabilistic clustering of a batch of input examples in one\npass. It estimates a distribution over the number of clusters $k$, and for each\n$1 \\leq k \\leq k_\\mathrm{max}$, a distribution over the individual cluster\nassignment for each data point. The network is trained in advance in a\nsupervised fashion on separate data to learn grouping by any perceptual\nsimilarity criterion based on pairwise labels (same/different group). It can\nthen be applied to different data containing different groups. We demonstrate\npromising performance on high-dimensional data like images (COIL-100) and\nspeech (TIMIT). We call this ``learning to cluster'' and show its conceptual\ndifference to deep metric learning, semi-supervise clustering and other related\napproaches while having the advantage of performing learnable clustering fully\nend-to-end.","url_abs":"http://arxiv.org/abs/1807.04001v1","url_pdf":"http://arxiv.org/pdf/1807.04001v1.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":"learning-neural-models-for-end-to-end","repo_url":"https://github.com/kutoga/learning2cluster","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}