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MNIST and Fashion MNIST are extremely popular for testing in the machinelearning space. Fashion MNIST improves on MNIST by introducing a harderproblem, increasing the diversity of testing sets, and more accuratelyrepresenting a modern computer vision task. In order to increase the dataquality of FashionMNIST, this paper investigates near duplicate images betweentraining and testing sets. Near-duplicates between testing and training setsartificially increase the testing accuracy of machine learning models. Thispaper identifies near-duplicate images in Fashion MNIST and proposes a datasetwith near-duplicates removed.

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