Handling noisy annotations in deep supervised learning

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.