The idea behind Generalized Max Pooling
I am trying to understand the idea of "Generalized Max Pooling". It seems they try to make the 'pooled' representation similar to the features. If so I feel some rare discriminating features could not be captured by the 'pooled' representation. The 'pooled' representation will tend to be similar to the most frequent features. It will not capture the 'max' feature.
Could you please explain this method.
Topic pooling cnn convolutional-neural-network deep-learning machine-learning
Category Data Science