@inproceedings{491d2609f6de426ca63a2616319c3523,
title = "ExGate: Externally Controlled Gating for Feature-based Attention in Artificial Neural Networks",
abstract = "The perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however, they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease the computational load by only processing pertinent parts of the stimuli. This paper addresses the implementation of top-down, feature-based attention in an artificial neural network by the use of externally controlled neuron gating. Our results showed a 5% increase in classification accuracy on the CIFAR-10 dataset versus a non-gated version while adding a limited number of parameters. Our gated model also produces more reasonable errors in predictions by drastically reducing the prediction of classes that belong to a different category than the true class.",
keywords = "attention, neural networks, perception",
author = "Jarryd Son and Mishra, {Amit Kumar}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Joint Conference on Neural Networks, IJCNN 2022 ; Conference date: 18-07-2022 Through 23-07-2022",
year = "2022",
month = sep,
day = "30",
doi = "10.1109/IJCNN55064.2022.9892449",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE Press",
booktitle = "2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings",
address = "United States of America",
}