Handling noisy annotations in deep supervised learning

Ichraq Lemghari; Sylvie Le-Hégarat; Emanuel Aldea; Jennifer Vandoni

doi:10.1117/12.2692547

28 July 2023 Handling noisy annotations in deep supervised learning

Ichraq Lemghari, Sylvie Le-Hégarat, Emanuel Aldea, Jennifer Vandoni

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490X (2023) https://doi.org/10.1117/12.2692547
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

Non-destructive testing (NDT) is employed by companies to assess the features of a material, in order to identify some variations or anomalies in its properties without causing any damage to the original object. In this context of industrial visual inspection, the help of new technologies and especially deep supervised learning is nowadays required to reach a very high level of performance. Data labelling, that is essential to reach such performance, may be fastidious and tricky, and only experts can provide the labelling of the material possible defects. Considering classification problems, this paper addresses the issue of handling noisy labels in datasets. We will first present the existing works related to the problem, our general idea of how to handle it, then we will present our proposed method in detail along with the obtained results that reach more than 0.96 and 0.88 of accuracy for noisified MNIST and CIFAR-10 respectively with a 40% noise ratio. Finally, we present some potential perspectives for future works.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ichraq Lemghari, Sylvie Le-Hégarat, Emanuel Aldea, and Jennifer Vandoni "Handling noisy annotations in deep supervised learning", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490X (28 July 2023); https://doi.org/10.1117/12.2692547

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KEYWORDS

Data modeling

Statistical modeling

Machine learning

Convolutional neural networks

Neural networks

Nondestructive evaluation

Performance modeling

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