ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
Krizhevsky, Sutskever, Hinton · 2012
"Deep CNNs trained on GPUs shatter the ImageNet benchmark, igniting the modern deep-learning era."
Every year, the ImageNet competition tested how well computer programs could correctly label photos into categories. For years, progress came from hand-engineered techniques designed by computer vision experts. In 2012, a deep convolutional neural network trained by Krizhevsky, Sutskever, and Hinton entered and won by such a wide margin that it upended the field's consensus: a network with enough layers, trained on enough labeled images, using specific practical tricks to make training feasible, could learn better visual features on its own than experts could design by hand.
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The Bitter Lesson
"General methods that leverage computation ultimately beat human-knowledge-engineered approaches in AI."