A New Global Pooling Method for Deep Neural Networks: Global Average of Top-K Max

Global Pooling (GP) is one of the important layers in deep neural networks. GP significantly reduces the number of model parameters by summarizing the feature maps and enables a reduction in the computational cost of training. The most commonly used GP methods are global max pooling (GMP) and global average pooling (GAP). The GMP method produces successful results in experimental studies but has a tendency to overfit training data and may not generalize well to test data. On the other hand, the GAP method takes into account all activations in the pooling region, which reduces the effect of high activation areas and causes a decrease in model performance. In this study, a GP method called global average of top-k max pooling (GAMP) is proposed, which returns the average of the highest k activations in the feature map and allows for mixing the two methods mentioned. The proposed method is compared quantitatively with other GP methods using different models, i.e., Custom and VGG16-based and different datasets, i.e., CIFAR10 and CIFAR100. The experimental results show that the proposed GAMP method provides better image classification accuracy than the other GP methods. When the Custom model is used, the proposed GAMP method provides a classification accuracy of 1.29% higher on the CIFAR10 dataset and 1.72% higher on the CIFAR100 dataset compared to the method with the closest performance.