RESEARCH

COMPUTER VISION

Cold Case: the Lost MNIST Digits

November 21, 2019

Abstract

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. In practice, this suggests that a shifting data distribution is far more dangerous than overusing an adequately distributed testing set.

Download the Paper

AUTHORS

Written by

Leon Bottou

Chavvi Yadav

Publisher

NeurIPS

Research Topics

Computer Vision

Related Publications

December 12, 2024

COMPUTER VISION

EvalGIM: A Library for Evaluating Generative Image Models

Melissa Hall, Oscar Mañas, Reyhane Askari, Mark Ibrahim, Candace Ross, Pietro Astolfi, Tariq Berrada Ifriqi, Marton Havasi, Yohann Benchetrit, Karen Ullrich, Carolina Braga, Abhishek Charnalia, Maeve Ryan, Mike Rabbat, Michal Drozdzal, Jakob Verbeek, Adriana Romero Soriano

December 12, 2024

December 11, 2024

COMPUTER VISION

Video Seal: Open and Efficient Video Watermarking

Pierre Fernandez, Hady Elsahar, Zeki Yalniz, Alexandre Mourachko

December 11, 2024

December 11, 2024

NLP

COMPUTER VISION

Meta CLIP 1.2

Hu Xu, Bernie Huang, Ellen Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Scott Yih, Philippe Brunet, Kim Hazelwood, Ramya Raghavendra, Daniel Li (FAIR), Saining Xie, Christoph Feichtenhofer

December 11, 2024

December 11, 2024

COMPUTER VISION

Measuring Deja Vu Memorization Efficiently

Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri

December 11, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.