Flexible, High Performance Convolutional Neural Networks for Image Classification
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.
Cite
Text
Ciresan et al. "Flexible, High Performance Convolutional Neural Networks for Image Classification." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-210Markdown
[Ciresan et al. "Flexible, High Performance Convolutional Neural Networks for Image Classification." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/ciresan2011ijcai-flexible/) doi:10.5591/978-1-57735-516-8/IJCAI11-210BibTeX
@inproceedings{ciresan2011ijcai-flexible,
title = {{Flexible, High Performance Convolutional Neural Networks for Image Classification}},
author = {Ciresan, Dan Claudiu and Meier, Ueli and Masci, Jonathan and Gambardella, Luca Maria and Schmidhuber, Jürgen},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1237-1242},
doi = {10.5591/978-1-57735-516-8/IJCAI11-210},
url = {https://mlanthology.org/ijcai/2011/ciresan2011ijcai-flexible/}
}