  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.
