%0 Conference Paper %1 krizhevsky2012imagenet %A Krizhevsky, Alex %A Sutskever, Ilya %A Hinton, Geoffrey E %B Advances in neural information processing systems %D 2012 %K %P 1097--1105 %T Imagenet classification with deep convolutional neural networks %U http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf %X 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 “dropout” 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.

@inproceedings{krizhevsky2012imagenet, 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 “dropout” 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.}, added-at = {2017-02-26T17:55:06.000+0100}, author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E}, biburl = {https://www.bibsonomy.org/bibtex/2784f6d0ddce5f78d5d2105a1781cecc2/nosebrain}, booktitle = {Advances in neural information processing systems}, interhash = {74bbb5dea5afb1b088bd10e317f1f0d2}, intrahash = {784f6d0ddce5f78d5d2105a1781cecc2}, keywords = {}, pages = {1097--1105}, timestamp = {2017-02-26T17:55:06.000+0100}, title = {Imagenet classification with deep convolutional neural networks}, url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}, year = 2012 }