{"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/loss-functions-for-top-k-error-analysis-and","title":"Loss Functions for Top-k Error: Analysis and Insights","arxiv_id":"1512.00486","date":"2015-12-01","proceeding":"CVPR 2016 6","authors":["Maksim Lapin","Matthias Hein","Bernt Schiele"],"abstract":"In order to push the performance on realistic computer vision tasks, the\nnumber of classes in modern benchmark datasets has significantly increased in\nrecent years. This increase in the number of classes comes along with increased\nambiguity between the class labels, raising the question if top-1 error is the\nright performance measure. In this paper, we provide an extensive comparison\nand evaluation of established multiclass methods comparing their top-k\nperformance both from a practical as well as from a theoretical perspective.\nMoreover, we introduce novel top-k loss functions as modifications of the\nsoftmax and the multiclass SVM losses and provide efficient optimization\nschemes for them. In the experiments, we compare on various datasets all of the\nproposed and established methods for top-k error optimization. An interesting\ninsight of this paper is that the softmax loss yields competitive top-k\nperformance for all k simultaneously. For a specific top-k error, our new top-k\nlosses lead typically to further improvements while being faster to train than\nthe softmax.","url_abs":"http://arxiv.org/abs/1512.00486v2","url_pdf":"http://arxiv.org/pdf/1512.00486v2.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":"loss-functions-for-top-k-error-analysis-and","repo_url":"https://github.com/mlapin/libsdca","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.00486","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}