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Poster
Cold Case: The Lost MNIST Digits
Chhavi Yadav · Leon Bottou

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #123
in Data, Challenges, Implementations, and Software -- Data Sets or Data Repositories »

Although the popular MNIST dataset \citep{mnist} is derived from the NIST database \citep{nist-sd19}, precise processing steps of 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 enable us to investigate the impact of twenty-five years of MNIST experiments on the reported testing performances. Our results unambiguously confirm the trends observed by \citet{recht2018cifar,recht2019imagenet}: 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.

Keywords: Data Sets or Data Repositories · Data, Challenges, Implementations, and Software · Algorithms -> Classification; Algorithms -> Large Margin Methods; Applications · Computer Vision; Data, Challenges, Implementa
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Author Information

Chhavi Yadav (NYU, Walmart Labs)
Leon Bottou (Facebook AI Research)

Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .

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