  Despite the superior performance of CNN, deploying them on low computational
power devices is still limited as they are typically computationally expensive.
One key cause of the high complexity is the connection between the convolution
layers and the fully connected layers, which typically requires a high number
of parameters. To alleviate this issue, Bag of Features (BoF) pooling has been
recently proposed. BoF learns a dictionary, that is used to compile a histogram
representation of the input. In this paper, we propose an approach that builds
on top of BoF pooling to boost its efficiency by ensuring that the items of the
learned dictionary are non-redundant. We propose an additional loss term, based
on the pair-wise correlation of the items of the dictionary, which complements
the standard loss to explicitly regularize the model to learn a more diverse
and rich dictionary. The proposed strategy yields an efficient variant of BoF
and further boosts its performance, without any additional parameters.
