Multi Layer Neural Networks as Replacement for Pooling Operations
This work addresses the need for more efficient and learnable pooling layers in neural networks, particularly for applications like semantic segmentation, though it is incremental as it builds on existing efforts to replace pooling operations.
The paper tackles the problem of replacing traditional pooling operations in neural networks with a simpler, parameter-efficient approach using a single perceptron, showing that this method is effective and reduces model complexity compared to tensor convolution with strides.
Pooling operations, which can be calculated at low cost and serve as a linear or nonlinear transfer function for data reduction, are found in almost every modern neural network. Countless modern approaches have already tackled replacing the common maximum value selection and mean value operations, not to mention providing a function that allows different functions to be selected through changing parameters. Additional neural networks are used to estimate the parameters of these pooling functions.Consequently, pooling layers may require supplementary parameters to increase the complexity of the whole model. In this work, we show that one perceptron can already be used effectively as a pooling operation without increasing the complexity of the model. This kind of pooling allows for the integration of multi-layer neural networks directly into a model as a pooling operation by restructuring the data and, as a result, learnin complex pooling operations. We compare our approach to tensor convolution with strides as a pooling operation and show that our approach is both effective and reduces complexity. The restructuring of the data in combination with multiple perceptrons allows for our approach to be used for upscaling, which can then be utilized for transposed convolutions in semantic segmentation.