Real Application of CNN Interpretation Methods: Document Image Classification Model Errors’ Detection and Validation

Real Application of CNN Interpretation Methods: Document Image Classification Model Errors’ Detection and Validation

Alexander Olegovich GOLODKOV, Oksana Vladimirovna BELYAEVA, Andrey Igorevich PERMINOV

Abstract

In this paper, we consider the case of applying convolutional neural networks interpretation methods to ResNet 18 model in order to identify and justify model errors. The model is used in the problem of classifying the orientation of text documents images. First, using interpretation methods, an assumption was made as to why the neural network shows low metrics on data that differs from training images. The alleged reason was the presence of artifacts on the generated training images, caused by the use of an image rotation function. Further, using the Vanilla Gradient, Guided Backpropagation, Integrated Gradients, GradCAM methods and the invented metric, we managed to accurately confirm the hypothesis put forward. The obtained results helped to significantly improve the accuracy of the model.

Keywords

CNN Interpretation, Document Image Classification, Document Orientation Detection

Edition

Proceedings of the Institute for System Programming, vol. 35, issue 2, 2023, 7-18

ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).

DOI: 10.15514/ISPRAS-2023-35(2)-1

For citation

Alexander Olegovich GOLODKOV, Oksana Vladimirovna BELYAEVA, Andrey Igorevich PERMINOV Real Application of CNN Interpretation Methods: Document Image Classification Model Errors’ Detection and Validation. Proceedings of the Institute for System Programming, vol. 35, issue 2, 2023, 7-18 DOI: 10.15514/ISPRAS-2023-35(2)-1.

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