Flexible, high performance convolutional neural networks for image classification
IDSIA, USI and SUPSI, Galleria 2, 6928 Manno-Lugano, Switzerland
International Joint Conference on Artificial Intelligence, IJCAI 2011, 2011
@inproceedings{ciresan2011flexible,
title={Flexible, high performance convolutional neural networks for image classification},
author={Ciresan, D.C. and Meier, U. and Masci, J. and Schmidhuber, J.},
booktitle={International Joint Conference on Artificial Intelligence, IJCAI (to appear 2011)},
year={2011}
}
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.
October 5, 2011 by hgpu
