OBJECT RECORD / AIM-DOC-2012-001
ImageNet Classification with Deep Convolutional Neural Networks
A conference paper joining a deep convolutional network, a large image benchmark, and GPU implementation.
CURATORIAL READING
Why it is here
Krizhevsky, Sutskever, and Hinton described training a large deep convolutional network on ImageNet, including architectural choices, a GPU implementation, and measures intended to reduce overfitting.
Evidence boundary
Keep the claim inside the experiment: one architecture, one competition dataset, and reported test errors. “Deep learning won” is a later story, not a result printed by this object.
08 / SCALE AN EXPERIMENT
Position in the guided path
From written rules to learned representations
A deep convolutional network meets ImageNet and a GPU implementation.
Back to the object room →PROVENANCE
Source trail
Advances in Neural Information Processing Systems 25 ·