Systems based on Deep Neural Networks (DNNs) are increasingly being used in industry. In the process of system operation, DNNs need to be updated in order to improve their performance. When updating DNNs, systems used in companies that require high reliability must have as few regressions as possible. Since the update of DNNs has a data-driven nature, it is difficult to suppress regressions as expected by developers. This paper identifies the requirements for DNN updating in industry and presents a case study using techniques to meet those requirements. In the case study, we worked on satisfying the requirement to update models trained on car images collected in Fujitsu assuming security applications without regression for a specific class. We were able to suppress regression by customizing the objective function based on NeuRecover, a DNN repair technique. Moreover, we discuss some of the challenges identified in the case study.

本研究解决了深度神经网络(DNN)更新过程中高可靠性系统中回归问题。通过案例研究，采用针对特定类别的自定义目标函数、基于NeuRecover的DNN修复技术，我们成功抑制了回归。研究还讨论了案例研究中遇到的一些挑战。

无回归修复深度神经网络模型的经验报告