Convolutional Neural Network Simplification Based on Feature Maps Selection

We present a feature maps selection method for convolutional neural network (CNN) which can keep the classifier performance when CNN is used as a feature extractor. This method aims to simplify the last subsampling layer of CNN by cutting the number of feature maps with Linear Discriminant Analysis (LDA). It is shown that our method can stabilize the classification accuracy and achieve runtime reduction by removing some feature maps of the last subsampling layer which have worst separability. And the result also lay the foundation for further simplification of CNN.