  We introduce a simple semi-supervised learning approach for images based on
in-painting using an adversarial loss. Images with random patches removed are
presented to a generator whose task is to fill in the hole, based on the
surrounding pixels. The in-painted images are then presented to a discriminator
network that judges if they are real (unaltered training images) or not. This
task acts as a regularizer for standard supervised training of the
discriminator. Using our approach we are able to directly train large VGG-style
networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL
datasets, where our approach obtains performance comparable or superior to
existing methods.
