ImageNet Classification with Deep Convolutional Neural Networks
Abstract
We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.
Cite
Text
Krizhevsky et al. "ImageNet Classification with Deep Convolutional Neural Networks." Neural Information Processing Systems, 2012.Markdown
[Krizhevsky et al. "ImageNet Classification with Deep Convolutional Neural Networks." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/krizhevsky2012neurips-imagenet/)BibTeX
@inproceedings{krizhevsky2012neurips-imagenet,
title = {{ImageNet Classification with Deep Convolutional Neural Networks}},
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {1097-1105},
url = {https://mlanthology.org/neurips/2012/krizhevsky2012neurips-imagenet/}
}