Implementation of FlexPooling with Simple Auxiliary Classifiers in DeepNetworks
ResNet VGGNet InceptionNet
In this paper, we propose a simple yet effective adaptive pooling method, referred to as FlexPooling, which generalizes the concept of average pooling by learning a weighted average pooling over the activations jointly with the rest of the network. Moreover, attaching the CNN with Simple Auxiliary Classifiers (SAC) further demonstrates the superiority of our method as compared to the standard methods. Finally, we show that our simple approach consistently outperforms baseline networks on multiple popular datasets in image classification, giving us around a 1-3% increase in accuracy.
