Multi Layer Neural Networks as Replacement for Pooling Operations
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Pooling operations are a layer found in almost every modern neural network, which can be calculated at low cost and serves as a linear or nonlinear transfer function for data reduction. Many modern approaches have already dealt with replacing the common maximum value selection and mean value operations by others or even to provide a function that includes different functions which can be selected through changing parameters. Additional neural networks are used to estimate the parameters of these pooling functions. Therefore, these pooling layers need many additional parameters and increase the complexity of the whole model. In this work, we show that already one perceptron can be used very effectively as a pooling operation without increasing the complexity of the model. This kind of pooling allows to integrate multi-layer neural networks directly into a model as a pooling operation by restructuring the data and thus learning complex pooling operations. We compare our approach to tensor convolution with strides as a pooling operation and show that our approach is effective and reduces complexity. The restructuring of the data in combination with multiple perceptrons allows also to use our approach for upscaling, which is used for transposed convolutions in semantic segmentation.
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