{"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/counterexample-guided-data-augmentation","title":"Counterexample-Guided Data Augmentation","arxiv_id":"1805.06962","date":"2018-05-17","proceeding":null,"authors":["Tommaso Dreossi","Shromona Ghosh","Xiangyu Yue","Kurt Keutzer","Alberto Sangiovanni-Vincentelli","Sanjit A. Seshia"],"abstract":"We present a novel framework for augmenting data sets for machine learning\nbased on counterexamples. Counterexamples are misclassified examples that have\nimportant properties for retraining and improving the model. Key components of\nour framework include a counterexample generator, which produces data items\nthat are misclassified by the model and error tables, a novel data structure\nthat stores information pertaining to misclassifications. Error tables can be\nused to explain the model's vulnerabilities and are used to efficiently\ngenerate counterexamples for augmentation. We show the efficacy of the proposed\nframework by comparing it to classical augmentation techniques on a case study\nof object detection in autonomous driving based on deep neural networks.","url_abs":"http://arxiv.org/abs/1805.06962v1","url_pdf":"http://arxiv.org/pdf/1805.06962v1.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":"counterexample-guided-data-augmentation","repo_url":"https://github.com/dreossi/analyzeNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"counterexample-guided-data-augmentation","repo_url":"https://github.com/BerkeleyLearnVerify/VerifAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.06962","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}