Fractal pooling algorithm based on fractal sum theory
Traditional pooling operations can neither objectively evaluate the differences among data in the pooled region nor effectively retain discriminative features in the pooled region.To solve these problems,a fractal pooling algorithm based on fractal sum theory is proposed,which can choose the optimal pooling strategy according to the variability among data in each channel of each feature map.Firstly,the fractal pooling operator and the back-propagation algorithm of training error are constructed by introducing the definition of fractal sum.The operator not only includes the max pooling and the average pooling,but also can reduce the training error.Then,during the implementation of the algorithm,the order is adaptively adjusted based on the differences between data in each channel of each feature map to determine the training weights for each data in the pooled region.Finally,a large number of classification performance experiments are carried out on different datasets and different architectures to verify that the proposed method achieves better classification results than traditional pooling methods and the mixed pooling.
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