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2012 NIPS NeurIPS 2012

ImageNet Classification with Deep Convolutional Neural Networks

Abstract

We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.

🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning and Machine Learning
🧭 Keyword Pioneer — gpu implementation
🐣 Hot Topic Early Bird — convolutional neural network
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
📈 Trend Setter — Neural Networks

Authors

Alex Krizhevsky , Ilya Sutskever , Geoffrey E. Hinton

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks Computer Vision > Analysis > Object Detection Deep Learning > Models > Neural Networks Deep Learning > Learning Types > Deep Learning Computer Vision > Core AI > Computer Vision Computer Vision > Analysis > Image Classification Deep Learning > Architectures > Convolutional Neural Networks

Keywords

neural network architecture image classification stochastic gradient descent feature learning computer vision deep learning image recognition gpu implementation neural network regularization convolutional neural network deep convolutional neural network deep neural network
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