{"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/wasserstein-barycenter-model-ensembling-1","title":"Wasserstein Barycenter Model Ensembling","arxiv_id":"1902.04999","date":"2019-02-13","proceeding":null,"authors":["Pierre Dognin","Igor Melnyk","Youssef Mroueh","Jerret Ross","Cicero dos Santos","Tom Sercu"],"abstract":"In this paper we propose to perform model ensembling in a multiclass or a\nmultilabel learning setting using Wasserstein (W.) barycenters. Optimal\ntransport metrics, such as the Wasserstein distance, allow incorporating\nsemantic side information such as word embeddings. Using W. barycenters to find\nthe consensus between models allows us to balance confidence and semantics in\nfinding the agreement between the models. We show applications of Wasserstein\nensembling in attribute-based classification, multilabel learning and image\ncaptioning generation. These results show that the W. ensembling is a viable\nalternative to the basic geometric or arithmetic mean ensembling.","url_abs":"http://arxiv.org/abs/1902.04999v1","url_pdf":"http://arxiv.org/pdf/1902.04999v1.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":"wasserstein-barycenter-model-ensembling-1","repo_url":"https://github.com/IBM/wasserstein-barycenters","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1902.04999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04999"}},"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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