Flexible, high performance convolutional neural networks for image classification

Authors:
Dan C. Cireşan;Ueli Meier;Jonathan Masci;Luca M. Gambardella;Jürgen Schmidhuber
Affiliations:
IDSIA, USI and SUPSI, Manno-Lugano, Switzerland;IDSIA, USI and SUPSI, Manno-Lugano, Switzerland;IDSIA, USI and SUPSI, Manno-Lugano, Switzerland;IDSIA, USI and SUPSI, Manno-Lugano, Switzerland;IDSIA, USI and SUPSI, Manno-Lugano, Switzerland
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Year:
2011

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Abstract

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.