本文深入探讨了卷积神经网络（CNN）在ImageNet数据集上取得显著成果的原因，包括大规模训练集、GPU的使用以及更有效的模型正则化策略如Dropout。文中详细解释了CNN的基本组成部分，从输入的二维图像到输出的概率向量覆盖不同类别的过程，并介绍了可视化技术通过反向映射活动到输入像素空间进行理解。
Large convolutional networks model on ImageNet
(Krizhevsky, A., Sutskever, I. and Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks , Advances in Neural Information Processing 25, 2012)
Why they perform so well?
How they migh be improved?
为什么CNN 在imagenet上取得如何显著的结果？可以归结为下面三个当面：
1、larger training sets; millions of labeled examples.
2、GPU makes practical
3、Better model regularization strategies: Dropout
输入：2D images; 输出：a probability vector over the C different classes.
Convolutional layers 包含：
(1) Convolution of the previous layer output with a set of learned filters
(2) Passing the responses through a rectified linear function (relu(x) = max(x,0)); Ensure the feature maps are always positive.
(3) Max-pooling over local neighborhoods (optionally)
(4) Normalize the responses across feature maps.(optionally)
Top few layers of NN: Fully-connected networks.
Final layer: a softmax classifier.
Visualization with a Deconvnet by mapping these activities back to the input pixel space.
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