{"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/deep-embedded-som-joint-representation","title":"Deep Embedded SOM: Joint Representation Learning and Self-Organization","arxiv_id":null,"date":"2019-04-24","proceeding":"ESANN 2019 2019 4","authors":["Florent Forest","Mustapha Lebbah","Hanene Azzag","Jérôme Lacaille"],"abstract":"In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map. Our model is composed of an autoencoder and a custom SOM layer that are optimized in a joint training procedure, motivated by the idea that the SOM prior could help learning SOM-friendly representations. We evaluate SOM-based models in terms of clustering quality and unsupervised clustering accuracy, and study the benefits of joint training.","url_abs":"https://www.i6doc.com/en/book/?gcoi=28001100931280","url_pdf":"http://florentfo.rest/files/ESANN-2019-DeepEmbeddedSOM-full-paper.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":"deep-embedded-som-joint-representation","repo_url":"https://github.com/FlorentF9/DESOM","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"imagedocument-clustering","task_name":"Image/Document Clustering"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-organized-clustering","task_name":"Self-Organized Clustering"}],"methods":[{"method_slug":"som","method_name":"SOM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}