  Raven's Progressive Matrices are multiple-choice intelligence tests, where
one tries to complete the missing location in a $3\times 3$ grid of abstract
images. Previous attempts to address this test have focused solely on selecting
the right answer out of the multiple choices. In this work, we focus, instead,
on generating a correct answer given the grid, without seeing the choices,
which is a harder task, by definition. The proposed neural model combines
multiple advances in generative models, including employing multiple pathways
through the same network, using the reparameterization trick along two pathways
to make their encoding compatible, a dynamic application of variational losses,
and a complex perceptual loss that is coupled with a selective backpropagation
procedure. Our algorithm is able not only to generate a set of plausible
answers, but also to be competitive to the state of the art methods in
multiple-choice tests.
