Authors
Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton
Publication date
2012
Conference
Advances in neural information processing systems
Pages
1097-1105
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 ...
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 ...
Total citations
Scholar articles
A Krizhevsky, I Sutskever, GE Hinton - Advances in neural information processing systems, 2012
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