Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Small changes in the production environment can have a negative impact on the performance of machine learning models. This study investigates the feasibility of various methods for detecting non-semantic Out-of-Distribution (OOD) cases in input images, which could be caused by hardware-side malfunctions, such as a defective camera flash. For this purpose, we design four experiments based on a real-world computer vision use case to simulate hardware problems that may occur in manufacturing and verify the performance of the various methods for detecting OOD cases. Furthermore, we explore the optimal sample size of input data to ensure that OOD cases can be found efficiently and successfully. The experimental results show that the tested methods can effectively and correctly detect the presence of non-semantic OOD data. The next step is to focus on securing ML models to identify malignant OOD cases, which negatively affects the performance of deep learning models.
Recommended Citation
Jin, Meng; Rajesh, Mehta Dharmil; Xing, Wenting; Schniertshauer, Johannes; Neuhüttler, Jens; and Zimmermann, Felix, "Implementation and Validation of non-semantic Out-of-Distribution Detection on Image Data in Manufacturing" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 5.
https://aisel.aisnet.org/hicss-56/st/cybersecurity_and_sw_assurance/5
Implementation and Validation of non-semantic Out-of-Distribution Detection on Image Data in Manufacturing
Online
Small changes in the production environment can have a negative impact on the performance of machine learning models. This study investigates the feasibility of various methods for detecting non-semantic Out-of-Distribution (OOD) cases in input images, which could be caused by hardware-side malfunctions, such as a defective camera flash. For this purpose, we design four experiments based on a real-world computer vision use case to simulate hardware problems that may occur in manufacturing and verify the performance of the various methods for detecting OOD cases. Furthermore, we explore the optimal sample size of input data to ensure that OOD cases can be found efficiently and successfully. The experimental results show that the tested methods can effectively and correctly detect the presence of non-semantic OOD data. The next step is to focus on securing ML models to identify malignant OOD cases, which negatively affects the performance of deep learning models.
https://aisel.aisnet.org/hicss-56/st/cybersecurity_and_sw_assurance/5