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arXiv · 1906.08255

Training on test data: Removing near duplicates in Fashion-MNIST

Christopher Geier
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Abstract

MNIST and Fashion MNIST are extremely popular for testing in the machine learning space. Fashion MNIST improves on MNIST by introducing a harder problem, increasing the diversity of testing sets, and more accurately representing a modern computer vision task. In order to increase the data quality of FashionMNIST, this paper investigates near duplicate images between training and testing sets. Near-duplicates between testing and training sets artificially increase the testing accuracy of machine learning models. This paper identifies near-duplicate images in Fashion MNIST and proposes a dataset with near-duplicates removed.

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Christopher Geier. 2019-06-19. Training on test data: Removing near duplicates in Fashion-MNIST. https://arxiv.org/abs/1906.08255

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