Detection and Quantization of Data Drift in Image Classification Neural Networks
Author:
Affiliation:
1. Southern Illinois University,School of Electrical, Computer, & Biomedical Engineering,Carbondale,IL,USA
2. Advanced Micro Devices, Inc,ML Computer Vision Group,Santa Clara,CA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10147894/10147917/10147972.pdf?arnumber=10147972
Reference20 articles.
1. Accuracy Performance Degradation in Image Classification Models due to Concept Drift
2. Image Noise Level Estimation by Employing Chi-Square Distribution
3. On Information and Sufficiency
4. A baseline for detecting misclassified and out-of-distribution examples in neural networks;hendrycks,2016
5. Albumentations: Fast and Flexible Image Augmentations
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1. An Enhanced YOLO Failure Detection Method;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15
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