  This paper proposes a novel self-supervised based Cut-and-Paste GAN to
perform foreground object segmentation and generate realistic composite images
without manual annotations. We accomplish this goal by a simple yet effective
self-supervised approach coupled with the U-Net based discriminator. The
proposed method extends the ability of the standard discriminators to learn not
only the global data representations via classification (real/fake) but also
learn semantic and structural information through pseudo labels created using
the self-supervised task. The proposed method empowers the generator to create
meaningful masks by forcing it to learn informative per-pixel as well as global
image feedback from the discriminator. Our experiments demonstrate that our
proposed method significantly outperforms the state-of-the-art methods on the
standard benchmark datasets.
