TY  - JOUR
AB  - Deep neural networks (DNNs) have widespread applications in industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, previous work has shown that the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations) for a deployed model are constantly changing. Therefore, ensuring robustness is crucial to enhance business and consumer confidence. Previous research focuses mostly on the data aspect of model variance. This article takes a holistic view of DNN robustness by summarizing the issues related to both data and software configuration variances. We also present a predictive framework using search-based optimization to generate representative variances for robust learning, considering data and configurations.
AU  - Zhang, G
AU  - Sun, J
AU  - Xu, F
AU  - Sui, Y
AU  - Bandara, HMND
AU  - Chen, S
AU  - Menzies, T
DA  - 2023/11/01
DO  - 10.1109/MIC.2023.3322283
EP  - 20
JO  - IEEE Internet Computing
PB  - IEEE COMPUTER SOC
PY  - 2023/11/01
SP  - 13
TI  - A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning
VL  - 27
Y1  - 2023/11/01
Y2  - 2026/08/03
ER  -