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Network In Network

2013-12-16Code Available0· sign in to hype

Min Lin, Qiang Chen, Shuicheng Yan

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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 modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.

Tasks

Face IdentificationGeneral ClassificationImage Classification

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
CIFAR-10Network in NetworkPercentage correct91.2—Unverified
CIFAR-100NiNPercentage correct64.3—Unverified
MNISTNiNPercentage error0.5—Unverified
SVHNNetwork in NetworkPercentage error2.35—Unverified

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