{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:10:33Z","timestamp":1766067033434},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>The platform migration and customization have become an indispensable process of deep neural network (DNN) development lifecycle. A high-precision but complex DNN trained in the cloud on massive data and powerful GPUs often goes through an optimization phase (e.g, quantization, compression) before deployment to a target device (e.g, mobile device). A test set that effectively uncovers the disagreements of a DNN and its optimized variant provides certain feedback to debug and further enhance the optimization procedure. However, the minor inconsistency between a DNN and its optimized version is often hard to detect and easily bypasses the original test set. This paper proposes DiffChaser, an automated black-box testing framework to detect untargeted\/targeted disagreements between version variants of a DNN. We demonstrate 1) its effectiveness by comparing with the state-of-the-art techniques, and 2) its usefulness in real-world DNN product deployment involved with quantization and optimization.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/800","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5772-5778","source":"Crossref","is-referenced-by-count":50,"title":["DiffChaser: Detecting Disagreements for Deep Neural Networks"],"prefix":"10.24963","author":[{"given":"Xiaofei","family":"Xie","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Kyushu University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haijun","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuekang","family":"Li","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"},{"name":"Zhejiang Sci-Tech University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Li","sequence":"additional","affiliation":[{"name":"Tianjin University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:55Z","timestamp":1564285915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/800"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/800","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}