A Sparse Pyramid Pooling Strategy
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In this paper, we introduce a more principled pooling strategy for the Convolutional Restricted Boltzmann Machine. In order to solve the information loss problem of pooling operation and inspired by the idea of spatial pyramid, we replace the probabilistic max-pooling with our sparse pyramid pooling, which produces outputs of different sizes for different pyramid levels. And then we use sparse coding method to aggregate the multi-level feature maps. The experimental results on KTH action dataset and Maryland dynamic scenes dataset show that the sparse pyramid pooling achieves superior performance to the conventional probabilistic max-pooling. In addition, our pooling strategy can effectively improve the performance of deep neural network on video classification.
KeywordsProbabilistic max-pooling Spatial pyramid pooling Sparse coding Deep neural network
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