@inproceedings{6e572904fb8f4fe782ac8e74fe6bd8f7,
title = "Continual Learning with Out-of-Distribution Data Detection for Defect Classification",
abstract = "We propose a framework for defect detection in production lines that leverages deep learning models, out-of-distribution (OOD) detection, and continual learning to address the challenges of unknown defects and catastrophic forgetting. The proposed method divides classifier training into chronicle tasks, each introducing new defect classes and leveraging OOD detection to classify unknown defects. We evaluate the framework on a highly unbalanced product defect dataset and demonstrated that it outperformed existing approaches, improving the average F-score by 10%. Our method also improve the performance of the PODNet and DER models, but not the WA model due to its poor performance on our dataset. These results suggest that the proposed method has the potential to improve defect detection in production lines, especially for small-quantity-wide-variety production scenarios.",
keywords = "Continual learning, out-of-distribution",
author = "Lin, {Cheng Hsueh} and Lin, {Chia Yu} and Wang, {Li Jen} and Kuo, {Ted T.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",
year = "2023",
doi = "10.1109/ICCE-Taiwan58799.2023.10226969",
language = "English",
series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "337--338",
booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
address = "United States",
}