{"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/better-robustness-by-more-coverage-1","title":"Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning","arxiv_id":null,"date":"2021-08-01","proceeding":"Findings (ACL) 2021 8","authors":["Chenglei Si","Zhengyan Zhang","Fanchao Qi","Zhiyuan Liu","Yasheng Wang","Qun Liu","Maosong Sun"],"abstract":"","url_abs":"https://aclanthology.org/2021.findings-acl.137","url_pdf":"https://aclanthology.org/2021.findings-acl.137.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":"better-robustness-by-more-coverage-1","repo_url":"https://github.com/thunlp/MixADA","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}