ImageNet classification with deep convolutional neural networks

@article{Krizhevsky2012ImageNetCW,
  title={ImageNet classification with deep convolutional neural networks},
  author={Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton},
  journal={Communications of the ACM},
  year={2012},
  volume={60},
  pages={84 - 90}
}
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. [] Key Method The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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References

SHOWING 1-10 OF 50 REFERENCES
ImageNet Classification with Deep Convolutional Neural
  • Computer Science, Environmental Science
  • 2013
The intended goal of the experiments was to create a deep, convolutional network that uses supervised learning to achieve better (lower) error rates than the rates previously observed, to identify…
Convolutional Deep Belief Networks on CIFAR-10
TLDR
Using a combination of locally-connected convolutional units and globally-connected units, as well as a few tricks to reduce the e ects of over tting, the DBN achieves state-of-the-art performance in the classi cation task of the CIFAR-10 subset of the tiny images dataset.
High-Performance Neural Networks for Visual Object Classification
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a…
Multi-column deep neural networks for image classification
TLDR
On the very competitive MNIST handwriting benchmark, this method is the first to achieve near-human performance and improves the state-of-the-art on a plethora of common image classification benchmarks.
Learning Multiple Layers of Features from Tiny Images
TLDR
It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, using a novel parallelization algorithm to distribute the work among multiple machines connected on a network.
Multi-column deep neural network for traffic sign classification
Convolutional networks and applications in vision
TLDR
New unsupervised learning algorithms, and new non-linear stages that allow ConvNets to be trained with very few labeled samples are described, including one for visual object recognition and vision navigation for off-road mobile robots.
Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
TLDR
The goal is to devise classifiers which can incorporate images and classes on-the-fly at (near) zero cost and to explore k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers.
Best practices for convolutional neural networks applied to visual document analysis
TLDR
A set of concrete bestpractices that document analysis researchers can use to get good results with neural networks, including a simple "do-it-yourself" implementation of convolution with a flexible architecture suitable for many visual document problems.
Learning methods for generic object recognition with invariance to pose and lighting
  • Yann LeCun, F. Huang, L. Bottou
  • Computer Science
    Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
  • 2004
TLDR
A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second and proved impractical, while convolutional nets yielded 16/7% error.
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