Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels

Author:

Lee Minyoung,Jeon Joohyoung,Lee HongchulORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference66 articles.

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3. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. arXiv preprint arXiv:1910.10045.

4. Asif, K., Zhang, L., Derrible, S., Indacochea, J. E., Ozevin, D., & Ziebart, B. (2020). Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs. Journal of Intelligent Manufacturing pp. 1–15.

5. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K. R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7), e0130140.

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