Network In Network

12/16/2013 ∙ by Min Lin, et al. ∙ 0

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.

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Code Repositories

python-caffe-custom-cifar-100-conv-net

Custom convolutional neural network on cifar-100 dataset for image classification. Images and their labels are processed to HDF5 data format for use in Caffe.


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network_in_network

Implementation of Network In Network by chainer (Network In Network: https://arxiv.org/abs/1312.4400)


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Network-in-Network

A keras implement of NIN


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