Multi-column Deep Neural Networks for Image Classification

13 Feb 2012  ·  Dan Cireşan, Ueli Meier, Juergen Schmidhuber ·

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 winner-take-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. read more

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Classification CIFAR-10 MCDNN Percentage correct 88.8 # 144
Traffic Sign Recognition GTSRB MCDNN Accuracy 99.5% # 3
Image Classification MNIST MCDNN Percentage error 0.23 # 7

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