不依赖回归测试的深度神经网络修复方法，提升工业系统可靠性。
An Experience Report on Regression-Free Repair of Deep Neural Network Model
- 基于NeuRecover定制目标函数，实现无回归更新
- 在富士通车载图像数据上成功抑制特定类别性能下降
- 适合高可靠性要求的工业级DNN维护场景
基于深度神经网络（DNN）的系统在工业中应用日益广泛。系统运行过程中需对DNN进行更新以提升性能，但企业在追求高可靠性时要求更新过程尽可能避免引入回归问题。由于DNN更新具有数据驱动特性，开发者难以有效控制回归风险。本文识别了工业界DNN更新的需求，并通过案例研究展示了满足这些需求的技术实践。案例中，针对富士通采集的车载图像数据训练的模型，在用于安全应用场景时，需保证特定类别无性能退化。我们通过基于NeuRecover的DNN修复技术，定制优化目标函数，成功抑制了回归现象。同时，论文还讨论了案例研究中发现的关键挑战。
原文摘要 · Abstract (English)
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.
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