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Instead, we build micro neural\nnetworks with more complex structures to abstract the data within the receptive\nfield. We instantiate the micro neural network with a multilayer perceptron,\nwhich is a potent function approximator. The feature maps are obtained by\nsliding the micro networks over the input in a similar manner as CNN; they are\nthen fed into the next layer. Deep NIN can be implemented by stacking mutiple\nof the above described structure. With enhanced local modeling via the micro\nnetwork, we are able to utilize global average pooling over feature maps in the\nclassification layer, which is easier to interpret and less prone to\noverfitting than traditional fully connected layers. 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