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ImageNet Classification with Deep Convolutional Neural ...

ImageNet Classification with Deep ConvolutionalNeural NetworksAlex KrizhevskyUniversity of SutskeverUniversity of E. HintonUniversity of trained a large, deep Convolutional Neural network to classify the millionhigh-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-ferent classes. On the test data, we achieved top-1 and top-5 error rates of which is considerably better than the previous state-of-the-art. Theneural network, which has 60 million parameters and 650,000 neurons, consistsof five Convolutional layers, some of which are followed by max-pooling layers,and three fully-connected layers with a final 1000-way softmax. To make train-ing faster, we used non-saturating neurons and a very efficient GPU implemen-tation of the convolution operation.

The learning rates for each net-work were chosen independently to make train-ing as fast as possible. No regularization of any kind was employed. The magnitude of the ... Linear Units (ReLUs). Deep convolutional neural net-works with ReLUs train several times faster than their equivalents with tanh units. This is demonstrated in Figure 1, which ...

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arxiv.org

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