NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History
NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History
复制标题
DOI：
10.48550/arxiv.2203.00191
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Sho Tokui;Susumu Tokumoto;Akihito Yoshii;F. Ishikawa;Takao Nakagawa;Kazuki Munakata;Shinji Kikuchi
中科院分区：
文献类型：
--
作者：
Sho Tokui;Susumu Tokumoto;Akihito Yoshii;F. Ishikawa;Takao Nakagawa;Kazuki Munakata;Shinji Kikuchi
鉴于对安全应用程序的需求不断提高，要提高深度神经网络的质量（DNN）至关重要。其他行为，导致回归，即，当工程师正确处理的输入时，更新的DNN失败了。需要调查针对安全或信任的密集保证活动的失败。实现足够的可控性来抑制DNN维修任务的回归。判断哪些DNN参数应更改或不抑制回归。当维修要求紧密以解决特定的失败时，我们的方法尤其有效。回归的％），在许多情况下，这是重新培训引起的回归的十分之一。
Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key challenge comes from the little controllability in updating DNNs. Retraining to fix some behavior often has a destructive impact on other behavior, causing regressions, i.e., the updated DNN fails with inputs correctly handled by the original one. This problem is crucial when engineers are required to investigate failures in intensive assurance activities for safety or trust. Search-based repair techniques for DNNs have potentials to tackle this challenge by enabling localized updates only on “responsible parameters” inside the DNN. However, the potentials have not been explored to realize sufficient controllability to suppress regressions in DNN repair tasks. In this paper, we propose a novel DNN repair method that makes use of the training history for judging which DNN parameters should be changed or not to suppress regressions. We implemented the method into a tool called Neurecover and evaluated it with three datasets. Our method outperformed the existing method by achieving often less than a quarter, even a tenth in some cases, number of regressions. Our method is especially effective when the repair requirements are tight to fix specific failure types. In such cases, our method showed stably low rates (<2 %) of regressions, which were in many cases a tenth of regressions caused by retraining.