Continual Learning with Out-of-Distribution Data Detection for Defect Classification

Cheng Hsueh Lin, Chia Yu Lin, Li Jen Wang, Ted T. Kuo

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

原文???core.languages.en_GB???
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面337-338
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態已出版 - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
持續時間: 17 7月 202319 7月 2023

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區Taiwan
城市Pingtung
期間17/07/2319/07/23

指紋

深入研究「Continual Learning with Out-of-Distribution Data Detection for Defect Classification」主題。共同形成了獨特的指紋。

引用此