Abstract
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. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. 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. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Keywords
Softmax functionConvolutional neural networkComputer sciencePoolingDropout (neural networks)Artificial intelligenceConvolution (computer science)Regularization (linguistics)Pattern recognition (psychology)Deep neural networksWord error rateNormalization (sociology)Artificial neural networkMachine learning
Affiliated Institutions
Related Publications
Striving for Simplicity: The All Convolutional Net
Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a sm...
Network In Network
Abstract: We propose a novel deep network structure called In Network (NIN) to enhance model discriminability for local patches within the receptive field. The conventional con...
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to bec...
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150...
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...
Publication Info
- Year
- 2017
- Type
- article
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- Citations
- 75536
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
75536
OpenAlex
Cite This
Alex Krizhevsky,
Ilya Sutskever,
Geoffrey E. Hinton
(2017).
ImageNet classification with deep convolutional neural networks.
Communications of the ACM
, 60
(6)
, 84-90.
https://doi.org/10.1145/3065386
Identifiers
- DOI
- 10.1145/3065386