Name
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%, respectively, 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/3065386 | Commun. ACM |
Field | DocType | Volume |
Softmax function,Convolutional neural network,Computer science,Word error rate,Caffè,Test data,Artificial intelligence,Deep learning,Artificial neural network,TrueNorth,Machine learning | Conference | 60 |
Issue | ISSN | Citations |
6 | 0001-0782 | 8030 |
PageRank | References | Authors |
252.79 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex Krizhevsky | 1 | 13175 | 588.91 |
| Ilya Sutskever | 2 | 25814 | 1120.24 |
| geoffrey e hinton | 3 | 40435 | 4751.69 |