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CiteWeb id: 20120000033

CiteWeb score: 4776

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.

The publication "ImageNet Classification with Deep Convolutional Neural Networks" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Computer Science it is included to the Top 1000. Additionally, the publicaiton "ImageNet Classification with Deep Convolutional Neural Networks" is placed in the Top 100 among other scientific works published in 2012.
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