  We argue that the estimation of mutual information between high dimensional
continuous random variables can be achieved by gradient descent over neural
networks. We present a Mutual Information Neural Estimator (MINE) that is
linearly scalable in dimensionality as well as in sample size, trainable
through back-prop, and strongly consistent. We present a handful of
applications on which MINE can be used to minimize or maximize mutual
information. We apply MINE to improve adversarially trained generative models.
We also use MINE to implement Information Bottleneck, applying it to supervised
classification; our results demonstrate substantial improvement in flexibility
and performance in these settings.
