A deep convolutional network wins the ImageNet challenge
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.
Historical — it happened, and the record is settled.
Subjects
- Deep learning · technology · not located
- ImageNet · technology · United States · not located
- Geoffrey Hinton · person · United Kingdom; Canada · not located
- The AI accelerator · technology · not located
Evidence
Partly verified — Core facts are sourced; some optional detail is deliberately absent.
ImageNet Classification with Deep Convolutional Neural Networks
An eight-layer convolutional network — five convolutional and three fully connected layers — trained on graphics processors with dropout regularisation achieved a top-5 error rate of 15.3% at ILSVRC 2012 against 26.2% for the second-placed entry, learning hierarchical features rather than using hand-designed ones. V55 verification basis: the paper was not fetched. The 15.3% and 26.2% figures and the architecture description were carried in multiple retrieved summaries and in a hosted copy of the paper itself, but the attribution in those summaries is to secondary explainers as often as to the paper, and a curator should confirm both error rates against the ILSVRC 2012 results table before publication.
Advances in Neural Information Processing Systems 25 (NIPS 2012) · 2012 · Partly verified
Krizhevsky, A., Sutskever, I. and Hinton, G. E., NIPS 2012; the ImageNet Large Scale Visual Recognition Challenge 2012 results
Recorded on Deep learning
- ImageNet is assembled and released
- A deep convolutional network wins the ImageNet challenge
- AlphaGo defeats Lee Sedol in Seoul
Related
Related because they share a subject in the Atlas — never because the text looks similar.
- AlphaGo defeats Lee Sedol in Seoul · 2016-03-09 to 2016-03-15
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