{"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/cost-effective-testing-of-a-deep-learning","title":"Cost-Effective Testing of a Deep Learning Model through Input Reduction","arxiv_id":null,"date":"2019-09-25","proceeding":null,"authors":["Jianyi Zhou","Feng Li","Jinhao Dong","Hongyu Zhang","Dan Hao"],"abstract":"With the increasing adoption of Deep Learning (DL) models in various applications, testing DL models is vitally important. However, testing DL models is costly and expensive, especially when developers explore alternative designs of DL models and tune the hyperparameters. To reduce testing cost, we propose to use only a selected subset of testing data, which is small but representative enough for quick estimation of the performance of DL models. Our approach, called DeepReduce, adopts a two-phase strategy. At first, our approach selects testing data for the purpose of satisfying testing adequacy. Then, it selects more testing data in order to approximate the distribution between the whole testing data and the selected data leveraging relative entropy minimization.\nExperiments with various DL models and datasets show that our approach can reduce the whole testing data to 4.6\\% on average, and can reliably estimate the performance of DL models. Our approach significantly outperforms the random approach, and is more stable and reliable than the state-of-the-art approach.","url_abs":"https://openreview.net/forum?id=S1xCcpNYPr","url_pdf":"https://openreview.net/pdf?id=S1xCcpNYPr","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":"cost-effective-testing-of-a-deep-learning","repo_url":"https://github.com/DeepReduce/DeepReduce","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}