Network In Network
原文:https://arxiv.org/abs/1312.4400
Abstract:
We propose a novel deep network structure called “Network In Network”(NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local mo
Network In Network:深度学习中的微神经网络解析

Network In Network (NIN) 提出了一种新的深度网络结构,使用微神经网络代替传统线性滤波器增强局部建模能力。通过全局平均池化减少过拟合,提供更易于解释的分类结果。NIN在CIFAR-10和CIFAR-100等数据集上表现出优秀的分类性能。
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