{"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/dotmark-a-benchmark-for-discrete-optimal","title":"DOTmark - A Benchmark for Discrete Optimal Transport","arxiv_id":"1610.03368","date":"2016-10-11","proceeding":null,"authors":["Jörn Schrieber","Dominic Schuhmacher","Carsten Gottschlich"],"abstract":"The Wasserstein metric or earth mover's distance (EMD) is a useful tool in\nstatistics, machine learning and computer science with many applications to\nbiological or medical imaging, among others. Especially in the light of\nincreasingly complex data, the computation of these distances via optimal\ntransport is often the limiting factor. Inspired by this challenge, a variety\nof new approaches to optimal transport has been proposed in recent years and\nalong with these new methods comes the need for a meaningful comparison.\n  In this paper, we introduce a benchmark for discrete optimal transport,\ncalled DOTmark, which is designed to serve as a neutral collection of problems,\nwhere discrete optimal transport methods can be tested, compared to one\nanother, and brought to their limits on large-scale instances. It consists of a\nvariety of grayscale images, in various resolutions and classes, such as\nseveral types of randomly generated images, classical test images and real data\nfrom microscopy.\n  Along with the DOTmark we present a survey and a performance test for a cross\nsection of established methods ranging from more traditional algorithms, such\nas the transportation simplex, to recently developed approaches, such as the\nshielding neighborhood method, and including also a comparison with commercial\nsolvers.","url_abs":"http://arxiv.org/abs/1610.03368v1","url_pdf":"http://arxiv.org/pdf/1610.03368v1.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":"dotmark-a-benchmark-for-discrete-optimal","repo_url":"https://github.com/nbonneel/network_simplex","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"dotmark","name":"DOTmark","full_name":"Discrete Optimal Transport Benchmark"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.03368","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}