  We introduce the matrix-based Renyi's $\alpha$-order entropy functional to
parameterize Tishby et al. information bottleneck (IB) principle with a neural
network. We term our methodology Deep Deterministic Information Bottleneck
(DIB), as it avoids variational inference and distribution assumption. We show
that deep neural networks trained with DIB outperform the variational objective
counterpart and those that are trained with other forms of regularization, in
terms of generalization performance and robustness to adversarial attack.Code
available at https://github.com/yuxi120407/DIB
