  Learning over multi-view data is a challenging problem with strong practical
applications. Most related studies focus on the classification point of view
and assume that all the views are available at any time. We consider an
extension of this framework in two directions. First, based on the BiGAN model,
the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from
multi-view inputs. Second, it can deal with missing views and is able to update
its prediction when additional views are provided. We illustrate these
properties on a set of experiments over different datasets.
