Show Reference: "Imagenet classification with deep convolutional neural networks"

Imagenet classification with deep convolutional neural networks In Advances in Neural Information Processing Systems, Vol. 25 (2012), pp. 1097-1105 by Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton edited by Fernando C. N. Pereira, Chris J. C. Burges, Léon Bottou, Kilian Q. Weinberger
    abstract = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the {ImageNet} {LSVRC}-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5\% and 17.0 \% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient {GPU} implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called \^{a}dropout\^{a} that proved to be very effective. We also entered a variant of this model in the {ILSVRC}-2012 competition and achieved a winning top-5 test error rate of 15.3\%, compared to 26.2 \% achieved by the second-best entry. 1},
    author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
    booktitle = {Advances in Neural Information Processing Systems},
    citeulike-article-id = {13133909},
    citeulike-linkout-0 = {},
    editor = {Pereira, Fernando C. N. and Burges, Chris J. C. and Bottou, L\'{e}on and Weinberger, Kilian Q.},
    keywords = {classification, deep-learning, learning, object},
    pages = {1097--1105},
    posted-at = {2015-03-17 10:54:56},
    priority = {2},
    publisher = {Curran Associates, Inc.},
    title = {Imagenet classification with deep convolutional neural networks},
    url = {},
    volume = {25},
    year = {2012}

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Convolutional neural networks make assumptions on the input:

  • stationarity of image statistics (the same statistics are true for all segments of an image),
  • locality of dependencies between pixels.

With a few strong but empirically correct assumptions on the input, CNNs buy us a reduction of the number of parameters and thus better training performance compared to standard feed-forward ANNs.

Advances in computing hardware and fast implementations of the necessary operations have made training of convolutional neural networks on very large data sets practical.

It took Kriszhevsky et al. five to six days to train their network on top-notch hardware available in 2012.

Krizhevsky et al. demonstrate that large, deep convolutional neural networks can have very good object classification performance.