text: This paper presents a framework to recover a set of independent mechanisms.  In order to do so it uses a set of experts each one made out of a GAN. \n\nMy main concern with this work is that I don't see any mechanism in the framework that prevents an expert  (or few of them) to win all examples except its own learning capacities.  p7 authors have also noticed that several experts fail to specialize  and I bet that is the reason why. \nThus, authors should analyze how well we can have all/most experts specialize in a pool vs expert capacity/architecture. \nIt would also be great to integrate a direct regularization mechanism in the cost  in order to do so.  Like for example a penalty in how many examples a expert has catched. \n\nMoreover, the discrimator D  (which is trained to discriminate between real or fake examples) seems to be directly used to tell if an example is throw from the targeted distribution.  It is not the same task.  How D will handle an example far from fake or real ones ?  Why will D answer negatively (or positively) on this example ?