{"url":"/method/r-mix","slug":"r-mix","name":"R-Mix","full_name":"Random Mix-up","full_name_withheld":false,"description_markdown":"R-Mix (Random Mix-up) is a Mix-up family Data Augmentation method. It combines random Mix-up with Saliency-guided mix-up, producing a procedure that is fast and performant, while reserving good characteristics of Saliency-guided Mix-up such as low Expected Calibration Error and high Weakly-supervised Object Localization accuracy.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Expeditious Saliency-guided Mix-up through Random Gradient Thresholding","paper":"/paper/expeditious-saliency-guided-mix-up-through","first_author":"Minh-Long Luu","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/expeditious-saliency-guided-mix-up-through"},"source":{"url":"https://arxiv.org/abs/2212.04875v3","title":"Expeditious Saliency-guided Mix-up through Random Gradient Thresholding","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Data Augmentation","url":"/methods/category/image-data-augmentation","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/expeditious-saliency-guided-mix-up-through","title":"Expeditious Saliency-guided Mix-up through Random Gradient Thresholding","date":"2022-12-09","arxiv_id":"2212.04875","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/classifier-calibration","name":"Classifier calibration","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/object-localization","name":"Object Localization","papers":1},{"task":"/task/weakly-supervised-object-localization","name":"Weakly-Supervised Object Localization","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2022","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/r-mix"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}