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. Alex Krizhevsky, et al., "ImageNet Classification with Deep …

To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convolutional Neural …

To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convolutional Neural Networks" https://papers.nips.cc…

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. Alex Krizhevs…

On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convolutional Neural Networks" https:…

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. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convol…