  Deep latent variable models are powerful tools for representation learning.
In this paper, we adopt the deep information bottleneck model, identify its
shortcomings and propose a model that circumvents them. To this end, we apply a
copula transformation which, by restoring the invariance properties of the
information bottleneck method, leads to disentanglement of the features in the
latent space. Building on that, we show how this transformation translates to
sparsity of the latent space in the new model. We evaluate our method on
artificial and real data.
