In this paper, regularized lightweight deep convolutional neural network models, capable of effectively operating in real-time on devices with restricted computational power for high-resolution video input are proposed. Furthermore, a novel regularization method motivated by the Quadratic Mutual Information, in order to improve the generalization ability of the utilized models is proposed. Extensive experiments on various binary classification problems involved in autonomous systems are performed, indicating the effectiveness of the proposed models as well as of the proposed regularizer.
@article{arxiv.2108.11774,
title = {Quadratic mutual information regularization in real-time deep CNN models},
author = {Maria Tzelepi and Anastasios Tefas},
journal= {arXiv preprint arXiv:2108.11774},
year = {2021}
}