.png){kind=icon}
返回
Distribution Aware Testing Framework for Deep Neural Networks
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3327820.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The increasing use of deep learning (DL) in safety-critical applications highlights the critical need for systematic and effective testing to ensure system reliability and quality. In this context, researchers have conducted various DL testing studies to identify weaknesses in Deep Neural Network (DNN) models, including exploring test coverage, generating challenging test inputs, and test selection. In this study, we propose a generic DNN testing framework that takes into consideration the distribution of test data and prioritizes them based on their potential to cause incorrect predictions by the tested DNN model. We evaluated the proposed framework using the image classification as a use case. We conducted empirical evaluations by implementing each phase with carefully chosen methods. We employed Variational Autoencoders to identify and eliminate out-of-distribution data from the test datasets. Additionally, we prioritize test data that increase uncertainty in the model, as these cases are more likely to reveal potential faults. The elimination of out-of-distribution data enables a more focused analysis to uncover the sources of DNN failures while using prioritized test data reduces the cost of test data labeling. Furthermore, we explored the use of post-hoc explainability methods to identify the cause of incorrect predictions, a process similar to debugging. This study can be a prelude to incorporating explainability methods into the model development process after testing.
Keyword:
Data distribution
deep learning testing
explainability
test selection and prioritization
uncertainty
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Molecular motion in plastic phases of organometallic compounds: iron-57 Mössbauer and carbon-13 n.m.r. spectra of tricarbonyl(1–5-hapto-cyclohexadienyl)iron tetrafluoroborate and related compounds有机金属化合物的塑料相中的分子运动: 铁-57 m ö ssbauer和碳-13 n.m.r.三羰基 (1-5-半环己二烯基) 四氟硼酸铁和相关化合物的光谱
PREAI
分享收藏分享收藏Review of Android Malware Detection Based on Deep Learning基于深度学习的Android恶意软件检测综述.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Intercellular Adhesion Molecule 1 (ICAM-1) Gene Variant is Associated with Coronary Artery Calcification Independent of Soluble ICAM-1 Levels细胞间粘附分子1 (ICAM-1) 基因变异与冠状动脉钙化相关，与可溶性ICAM-1水平无关
PREAI
分享收藏
查看更多内容