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Multi-column deep neural networks for image classification (2012)
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| Venue: | IN PROCEEDINGS OF THE 25TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2012 |
| Citations: | 151 - 9 self |
BibTeX
@INPROCEEDINGS{Ciresan12multi-columndeep,
author = {Dan Ciresan and Ueli Meier and Jürgen Schmidhuber},
title = {Multi-column deep neural networks for image classification},
booktitle = {IN PROCEEDINGS OF THE 25TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2012},
year = {2012},
pages = {3642--3649},
publisher = {}
}
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Abstract
Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winnertake-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.
Keyphrases
multi-column deep neural network image classification handwritten digit connected neural layer different way near-human performance graphic card visual cortex cannot match human performance traffic sign fast training receptive field computer vision large network depth traditional method common image classification benchmark winner neuron traffic sign recognition benchmark competitive mnist handwriting benchmark convolutional winnertake-all neuron
