Restricted stochastic pooling for convolutional neural network

Deep learning has been demonstrated to be a universal and successful tool for classification, detection and segmentation. Convolutional neural network (CNN), as a representative model, achieves excellent performance in many image process areas due to its special structural design---shared weights, local receptive field and pooling layer. Large efforts are devoted to studying and improving model performance based on these three parts. In this paper, we proposed a novel pooling layer named restricted stochastic pooling layer. Restricted stochastic pooling randomly select activation from the top n maximum of each pooling region instead of the largest one (max pooling), or stochastic one (stochastic pooling). Our proposed method can not only obtain representative activation but also add randomness to the model. Through large amounts of experiments, we achieve good performance on SVHN and CIFAR-10 which demonstrates the effectiveness of restricted stochastic pooling.