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Training on test data: Removing near duplicates in Fashion-MNIST (1906.08255v1)
Published 19 Jun 2019 in cs.LG, cs.CV, and stat.ML
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.