  We present generalized versions of the commonly used maximum pooling
operation: $k$th maximum and sorted pooling operations which selects the $k$th
largest response in each pooling region, selecting locally consistent features
of the input images. This method is able to increase the generalization power
of a network and can be used to decrease training time and error rate of
networks and it can significantly improve accuracy in case of training
scenarios where the amount of available data is limited, like one-shot learning
scenarios
