Training on test data: Removing near duplicates in Fashion-MNIST
Machine Learning
2019-06-21 v1 Computer Vision and Pattern Recognition
Machine Learning
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.
Keywords
Cite
@article{arxiv.1906.08255,
title = {Training on test data: Removing near duplicates in Fashion-MNIST},
author = {Christopher Geier},
journal= {arXiv preprint arXiv:1906.08255},
year = {2019}
}