DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification

Author:

Saifullah SaifullahORCID,Agne StefanORCID,Dengel AndreasORCID,Ahmed SherazORCID

Publisher

Springer Nature Switzerland

Reference59 articles.

1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012). https://doi.org/10.1109/TPAMI.2012.120

2. Afzal, M., Kolsch, A., Ahmed, S., Liwicki, M.: Cutting the error by half: investigation of very deep CNN and advanced training strategies for document image classification. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 883–888. IEEE Computer Society, Los Alamitos, CA, USA, November 2017. https://doi.org/10.1109/ICDAR.2017.149, https://doi.ieeecomputersociety.org/10.1109/ICDAR.2017.149

3. Afzal, M.Z., et al.: Deepdocclassifier: document classification with deep convolutional neural network. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1111–1115 (2015). https://doi.org/10.1109/ICDAR.2015.7333933

4. Brini, I., Mehri, M., Ingold, R., Essoukri Ben Amara, N.: An end-to-end framework for evaluating explainable deep models: application to historical document image segmentation. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds.) Computational Collective Intelligence, vol. 13501, pp. 106–119. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16014-1_10

5. Brock, A., De, S., Smith, S.L., Simonyan, K.: High-performance large-scale image recognition without normalization. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 1059–1071. PMLR, 18–24 July 2021. https://proceedings.mlr.press/v139/brock21a.html

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