Cold Case: the Lost MNIST Digits

Neural Information Processing Systems (NeurIPS)


Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy. We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc. We also reconstruct the complete MNIST test set with 60,000 samples instead of the usual 10,000. Since the balance 50,000 were never distributed, they can be used to investigate the impact of twenty-five years of MNIST experiments on the reported testing performances. Our limited results unambiguously confirm the trends observed by Recht et al. [2018, 2019]: although the misclassification rates are slightly off, classifier ordering and model selection remain broadly reliable. We attribute this phenomenon to the pairing benefits of comparing classifiers on the same digits.

Latest Publications

Log-structured Protocols in Delos

Mahesh Balakrishnan, Mihir Dharamshi, David Geraghty, Santosh Ghosh, Filip Gruszczynski, Jun Li, Jingming Liu, Suyog Mapara, Rajeev Nagar, Ivailo Nedelchev, Francois Richard, Chen Shen, Yee Jiun Song, Rounak Tibrewal, Vidhya Venkat, Ahmed Yossef, Ali Zaveri