{"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/variational-wasserstein-clustering","title":"Variational Wasserstein Clustering","arxiv_id":"1806.09045","date":"2018-06-23","proceeding":"ECCV 2018 9","authors":["Liang Mi","Wen Zhang","Xianfeng GU","Yalin Wang"],"abstract":"We propose a new clustering method based on optimal transportation. We solve\noptimal transportation with variational principles, and investigate the use of\npower diagrams as transportation plans for aggregating arbitrary domains into a\nfixed number of clusters. We iteratively drive centroids through target domains\nwhile maintaining the minimum clustering energy by adjusting the power\ndiagrams. Thus, we simultaneously pursue clustering and the Wasserstein\ndistances between the centroids and the target domains, resulting in a\nmeasure-preserving mapping. We demonstrate the use of our method in domain\nadaptation, remeshing, and representation learning on synthetic and real data.","url_abs":"http://arxiv.org/abs/1806.09045v4","url_pdf":"http://arxiv.org/pdf/1806.09045v4.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":"variational-wasserstein-clustering","repo_url":"https://github.com/icemiliang/vot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"variational-wasserstein-clustering","repo_url":"https://github.com/icemiliang/pyvot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.09045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.09045"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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