* [Refactor] Move transforms in mmselfsup to mmpretrain. * Update transform docs and configs. And register some mmcv transforms in mmpretrain. * Fix missing transform wrapper. * update selfsup transforms * Fix UT * Fix UT * update gaussianblur inconfigs --------- Co-authored-by: fangyixiao18 <fangyx18@hotmail.com> |
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README.md | ||
lenet5_mnist.py |
README.md
LeNet
Abstract
The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.

Citation
@ARTICLE{6795724,
author={Y. {LeCun} and B. {Boser} and J. S. {Denker} and D. {Henderson} and R. E. {Howard} and W. {Hubbard} and L. D. {Jackel}},
journal={Neural Computation},
title={Backpropagation Applied to Handwritten Zip Code Recognition},
year={1989},
volume={1},
number={4},
pages={541-551},
doi={10.1162/neco.1989.1.4.541}}
}