  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.
