Pooling Revisited: Your Receptive Field is Suboptimal

The size and shape of the receptive field determine how the network aggregates local features, and affect the overall performance of a model considerably. Many components in a neural network, such as depth, kernel sizes, and strides for convolution and pooling, influence the receptive field. However, they still rely on hyperparameters, and the receptive fields of existing models result in suboptimal shapes and sizes. Hence, we propose a simple yet effective Dynamically Optimized Pooling operation, referred to as DynOPool, which learns the optimized scale factors offeature maps end-to-end. Moreover, DynOPool determines the proper resolution of a feature map by learning the desirable size and shape of its receptive field, which allows an operator in a deeper layer to observe an input image in the optimal scale. Any kind of resizing modules in a deep neural network can be replaced by DynOPool with minimal cost. Also, DynOPool controls the complexity of the model by introducing an additional loss term that constrains computational cost. Our experiments show that the models equipped with the proposed learnable resizing module outperform the baseline algorithms on multiple datasets in image classification and semantic segmentation.