Efficient CNN with uncorrelated Bag of Features pooling

TL;DR

This paper introduces an improved Bag of Features pooling method for CNNs that enhances efficiency and diversity of the learned dictionary, enabling deployment on low-power devices.

Contribution

It proposes a novel loss term to reduce redundancy in the dictionary, improving CNN efficiency without adding extra parameters.

Findings

Enhanced CNN performance with non-redundant dictionary

Reduced computational complexity for low-power devices

No additional parameters needed for the proposed method

Abstract

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.