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AlexNet

AlexNet
Developer(s)Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton
Initial releaseJun 28, 2011
Repositorycode.google.com/archive/p/cuda-convnet/
Written inCUDA, C++
TypeConvolutional neural network
LicenseNew BSD License
AlexNet architecture and a possible modification. On the top is half of the original AlexNet (which is split into two halves, one per GPU). On the bottom is the same architecture but with the last "projection" layer replaced by another one that projects to fewer outputs. If one freezes the rest of the model and only finetune the last layer, one can obtain another vision model at cost much less than training one from scratch.
AlexNet block diagram

AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto.[when?] It had 60 million parameters and 650,000 neurons.[1]

The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.[1]

The three formed team SuperVision[2] and submitted AlexNet in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012.[3] The network achieved a top-5 error of 15.3%, more than 10.8 percentage points better than that of the runner-up.

The architecture influenced a large number of subsequent work in deep learning, especially in applying neural networks to computer vision.

  1. ^ a b Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. (2017-05-24). "ImageNet classification with deep convolutional neural networks" (PDF). Communications of the ACM. 60 (6): 84–90. doi:10.1145/3065386. ISSN 0001-0782. S2CID 195908774.
  2. ^ Large Scale Visual Recognition Challenge 2012 (ILSVRC2012)
  3. ^ "ImageNet Large Scale Visual Recognition Competition 2012 (ILSVRC2012)". image-net.org.

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