An Improved Method of Identifying Mislabeled Data and the Mislabeled Data in MNIST and CIFAR-10 Appendix Findings in Fashion-MNIST

8 Pages Posted: 12 Jan 2018

See all articles by Xinbin Zhang

Xinbin Zhang

Beijing University of Posts and Telecommunications (BUPT)

Date Written: January 5, 2018

Abstract

Objects classification is an important part of machine learning and the quality of the training data plays an important role in it. Some mislabeled data detection techniques have been proposed; however, there is no such work done on MNIST and CIFAR-10, the result on which is an important criterion for a machine learning models or algorithms. In this paper I develop an improved method to identify mislabeled data and find 675 mislabeled data in MNIST, 118 mislabeled data in CIFAR-10, some mislabeled data in fashion MNIST.

Suggested Citation

Zhang, Xinbin, An Improved Method of Identifying Mislabeled Data and the Mislabeled Data in MNIST and CIFAR-10 Appendix Findings in Fashion-MNIST (January 5, 2018). Available at SSRN: https://ssrn.com/abstract=3097307 or http://dx.doi.org/10.2139/ssrn.3097307

Xinbin Zhang (Contact Author)

Beijing University of Posts and Telecommunications (BUPT) ( email )

No 10, Xitucheng Road
Haidian District
Beijing, 100876
China

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