  The information bottleneck principle provides an information-theoretic method
for representation learning, by training an encoder to retain all information
which is relevant for predicting the label while minimizing the amount of
other, excess information in the representation. The original formulation,
however, requires labeled data to identify the superfluous information. In this
work, we extend this ability to the multi-view unsupervised setting, where two
views of the same underlying entity are provided but the label is unknown. This
enables us to identify superfluous information as that not shared by both
views. A theoretical analysis leads to the definition of a new multi-view model
that produces state-of-the-art results on the Sketchy dataset and label-limited
versions of the MIR-Flickr dataset. We also extend our theory to the
single-view setting by taking advantage of standard data augmentation
techniques, empirically showing better generalization capabilities when
compared to common unsupervised approaches for representation learning.
