{"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/clustering-oriented-representation-learning","title":"Clustering-Oriented Representation Learning with Attractive-Repulsive Loss","arxiv_id":"1812.07627","date":"2018-12-18","proceeding":null,"authors":["Kian Kenyon-Dean","Andre Cianflone","Lucas Page-Caccia","Guillaume Rabusseau","Jackie Chi Kit Cheung","Doina Precup"],"abstract":"The standard loss function used to train neural network classifiers,\ncategorical cross-entropy (CCE), seeks to maximize accuracy on the training\ndata; building useful representations is not a necessary byproduct of this\nobjective. In this work, we propose clustering-oriented representation learning\n(COREL) as an alternative to CCE in the context of a generalized\nattractive-repulsive loss framework. COREL has the consequence of building\nlatent representations that collectively exhibit the quality of natural\nclustering within the latent space of the final hidden layer, according to a\npredefined similarity function. Despite being simple to implement, COREL\nvariants outperform or perform equivalently to CCE in a variety of scenarios,\nincluding image and news article classification using both feed-forward and\nconvolutional neural networks. Analysis of the latent spaces created with\ndifferent similarity functions facilitates insights on the different use cases\nCOREL variants can satisfy, where the Cosine-COREL variant makes a consistently\nclusterable latent space, while Gaussian-COREL consistently obtains better\nclassification accuracy than CCE.","url_abs":"http://arxiv.org/abs/1812.07627v1","url_pdf":"http://arxiv.org/pdf/1812.07627v1.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":"clustering-oriented-representation-learning","repo_url":"https://github.com/kiankd/corel2019","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"representation-learning","task_name":"Representation 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}