ImageNet Classification with Deep Convolutional Neural Networks
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.
Summary¶
Introduces the deep convolutional network later known as AlexNet and demonstrates a large jump in ImageNet classification accuracy using GPUs, ReLUs, dropout, data augmentation, and a large labeled dataset. The paper's historical importance is that it made deep supervised representation learning the dominant computer-vision recipe, while its architectural details became the baseline vocabulary for later CNNs.