A deep convolutional network wins the ImageNet challenge

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The network entered by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton achieved a top-5 error rate of 15.3% on the ImageNet Large Scale Visual Recognition Challenge against 26.2% for the runner-up — a margin large enough that the method, rather than the increment, became the story. It was an eight-layer convolutional network trained on graphics processors with dropout regularisation, and it learned its own features rather than being given hand-designed ones. Within about two years essentially every serious entry in computer vision was a deep network. This is the conventional starting point for the present era, and it is a defensible one: it is the moment a public, externally scored, held-out test was won by a wide margin by a method the field had largely written off.

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