  Image completion is the problem of generating whole images from fragments
only. It encompasses inpainting (generating a patch given its surrounding),
reverse inpainting/extrapolation (generating the periphery given the central
patch) as well as colorization (generating one or several channels given other
ones). In this paper, we employ a deep network to perform image completion,
with adversarial training as well as perceptual and completion losses, and call
it the ``missing data encoder'' (MDE). We consider several configurations based
on how the seed fragments are chosen. We show that training MDE for ``random
extrapolation and colorization'' (MDE-REC), i.e. using random
channel-independent fragments, allows a better capture of the image semantics
and geometry. MDE training makes use of a novel ``hide-and-seek'' adversarial
loss, where the discriminator seeks the original non-masked regions, while the
generator tries to hide them. We validate our models both qualitatively and
quantitatively on several datasets, showing their interest for image
completion, unsupervised representation learning as well as face occlusion
handling.
