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Incorrectly labelled instances can significantly degrade both the performance of the machine learning model and the quality of its assessment. In this work, we describe a system for identifying mislabelled images. On the CIFAR-10 dataset, the achieved \(F_1\) score is higher by ten percentage points compared to another state-of-the-art result. Additionally, a new data augmentation scheme based on the selection of intensities for each transformation and the purity of an image is proposed and applied to the analyzed problem.

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Identification of Mislabelled Images with Data Augmentation

  • 会议论文

  • 发表日期：

    2025年

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  • Jakub Kozieł,
  • Patryk Tomaszewski,
  • Stanisław Kaźmierczak,
  • Jacek Mańdziuk

摘要

Incorrectly labelled instances can significantly degrade both the performance of the machine learning model and the quality of its assessment. In this work, we describe a system for identifying mislabelled images. On the CIFAR-10 dataset, the achieved \(F_1\) score is higher by ten percentage points compared to another state-of-the-art result. Additionally, a new data augmentation scheme based on the selection of intensities for each transformation and the purity of an image is proposed and applied to the analyzed problem.