DeepRepair: Style-guided repairing for deep neural networks in the real-world operational environment

• Main Authors: YU, Bing, QI, Hua, QING, Guo, JUEFEI-XU, Felix, XIE, Xiaofei, MA, Lei, ZHAO, Jianjun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Training; Maintenance engineering; Software; Data models; Training data; Neurons; Games; Data augmentation; deep neural network (DNN) repairing; deep neural network; operational environment; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7051; https://ink.library.smu.edu.sg/context/sis_research/article/8054/viewcontent/camera_ready.pdf
• Institution: Singapore Management University

Deep neural networks (DNNs) are continuously expanding their application to various domains due to their high performance. Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions...