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ImageNet Classification with Deep Convolutional Neural Networks
| dc.contributor.author | Krizhevsky, Alex | |
| dc.contributor.author | Sutskever, Ilya | |
| dc.contributor.author | Hinton, Geoffrey E. | |
| dc.date.accessioned | 2025-06-02T11:05:20Z | |
| dc.date.available | 2025-06-02T10:42:49Z | |
| dc.date.available | 2025-06-02T11:05:20Z | |
| dc.date.issued | 2012-12-03 | |
| dc.description.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. | |
| dc.identifier.uri | https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf | |
| dc.identifier.uri | http://data.inu.ac.kr/handle/123456789/1933.2 | |
| dc.language.iso | en_US | |
| dc.publisher | NeurIPS | |
| dc.relation.ispartofseries | 25 | |
| dc.subject | CNN | |
| dc.subject | ImageNet | |
| dc.subject | Deep Learning | |
| dc.subject | AlexNet | |
| dc.subject | Computer Vision | |
| dc.title | ImageNet Classification with Deep Convolutional Neural Networks | |
| dc.title.alternative | CNN, AlexNet | |
| dc.type | Article |
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