Application of Learned OWA Operators in Pooling and Channel Aggregation Layers in Convolutional Neural Networks

Promising results have been obtained in recent years when using OWA operators to aggregate data within CNNs pool layers, training their weights, instead of using the more usual operators (max and mean). OWA operators were also used to learn channel wise information from a certain layer, and the newly generated information is used to complement the input data for the following layer. The purpose of this article is to analyze and combine the two mentioned ideas. In addition to using the channel wise information generated by trainable OWA operators to complement the input data, replacement will also be analyzed. Several tests have been done to evaluate the performance change when applying OWA operators to classify images using VGG13 model.