  We propose a new approach to train a variational information bottleneck (VIB)
that improves its robustness to adversarial perturbations. Unlike the
traditional methods where the hard labels are usually used for the
classification task, we refine the categorical class information in the
training phase with soft labels which are obtained from a pre-trained reference
neural network and can reflect the likelihood of the original class labels. We
also relax the Gaussian posterior assumption in the VIB implementation by using
the mutual information neural estimation. Extensive experiments have been
performed with the MNIST and CIFAR-10 datasets, and the results show that our
proposed approach significantly outperforms the benchmarked models.
