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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky University of Toronto [email protected]
Ilya Sutskever University of Toronto [email protected]
GeoffreyE. Hinton University of Toronto [email protected]
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% 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.
本文训练了一个大规模的深度卷积神经网络来将ImageNet LSVRC-2010比赛中的包含120万幅高分辨率的图像数据集分为1000种不同类别。在测试集上，本文所得的top-1和top-5错误率分别为37.5%和17.0%，该测试结果大大优于当前的最佳水平。本文的神经网络包含6千万个参数和65万个神经元，包含了5个卷积层，其中有几层后面跟着最大池化（max-pooling）层，以及3个全连接层，最后还有一个1000路的softmax层。为了加快训练速度，本文使用了不饱和神经元以及一种高效的基于GPU的卷积运算方法。为了减少全连接层的过拟合，本文采用了最新的正则化方法“dropout”，该方法被证明非常有效。我们以该模型的变体参加了ILSVRC-2012比赛，相比第二名26.2%，我们以15.3%的top-5测试错误率获胜。
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Figure 2: An illustration of the architecture of our CNN, explicitly showing the delineation of responsibilities between the two GPUs. One GPU runs the layer-parts at the top of the figure while the other runs the layer-parts at the bottom. The GPUs communicate only at certain layers. The network’s inputis150,528-dimensional, and the number of neurons in the network’s remaining layers is given by 253,440–186,624–64,896–64,896–43,264– 4096–4096–1000.
图2 本文CNN的结构图示，明确地描述了两个GPU之间的职责。一个GPU运行图上方的层，另一个运行图下方的层。两个GPU只在特定的层通信。网络的输入是150,528维的，网络剩余层中的神经元数目分别是253440，186624，64896，64896，43264，4096，4096，1000
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References
[1] R.M.BellandY.Koren.Lessons from the net flixprize challenge. ACM SIG KDD Explorations News letter, 9(2):75–79, 2007.
[2] A. Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge 2010. www.imagenet.org/challenges. 2010.
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致谢：https://blog.csdn.net/qianqing13579/article/details/71381016?locationNum=15&fps=1
