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Research · November 1998

LeNet-5 and MNIST

The paper set out a complete convolutional architecture for document recognition and, with it, a handwritten digit set that became a shared ruler.

Why it matters

The architecture and the dataset AlexNet was built on fourteen years later appear here in finished form.

LeNet-5 has seven layers not counting the input (three convolutional, two subsampling, one fully connected and an output layer) and trains end to end by gradient, from pixels to answer. The authors assembled the 70,000-image MNIST set from NIST digit databases: SD-3 was written by Census Bureau employees, SD-1 by high-school students. It is still the most common teaching example. The paper also argues a broader thesis: a document is better recognised by one trained system than by a chain of separately tuned steps.

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November 1998
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Proceedings of the IEEE volume 86, issue 11, November 1998.

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