Global-Local Processing in Convolutional Neural Networks
Zahra Rezvani Soroor Shekarizeh Mohammad Sabokrou
Convolutional Neural Networks (CNNs) have achieved outstanding performance on
image processing challenges. Actually, CNNs imitate the typically developed
human brain structures at the micro-level (Artificial neurons). At the same
time, they distance themselves from imitating natural visual perception in
humans at the macro architectures (high-level cognition). Recently it has been
investigated that CNNs are highly biased toward local features and fail to
detect the global aspects of their input. Nevertheless, the literature offers
limited clues on this problem. To this end, we propose a simple yet effective
solution inspired by the unconscious behavior of the human pupil. We devise a
simple module called Global Advantage Stream (GAS) to learn and capture the
holistic features of input samples (i.e., the global features). Then, the GAS
features were combined with a CNN network as a plug-and-play component called
the Global/Local Processing (GLP) model. The experimental results confirm that
this stream improves the accuracy with an insignificant additional
computational/temporal load and makes the network more robust to adversarial
attacks. Furthermore, investigating the interpretation of the model shows that
it learns a more holistic representation similar to the perceptual system of
healthy humans