  We tackle the problem of object discovery, where objects are segmented for a
given input image, and the system is trained without using any direct
supervision whatsoever. A novel copy-pasting GAN framework is proposed, where
the generator learns to discover an object in one image by compositing it into
another image such that the discriminator cannot tell that the resulting image
is fake. After carefully addressing subtle issues, such as preventing the
generator from `cheating', this game results in the generator learning to
select objects, as copy-pasting objects is most likely to fool the
discriminator. The system is shown to work well on four very different
datasets, including large object appearance variations in challenging cluttered
backgrounds.
