Network In Network
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 acti- vation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We in- stantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro net- works 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 uti- lize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected lay- ers. 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.
Comparison of linear convolution layer and mlpconv layer
Inear_convolution_layer_vs_mlpconv_layer.png
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- Network In Network (Min Lin, Qiang Chen, Shuicheng Yan)
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1312.4400v3.pdf