  Image Completion refers to the task of filling in the missing regions of an
image and Image Extrapolation refers to the task of extending an image at its
boundaries while keeping it coherent. Many recent works based on GAN have shown
progress in addressing these problem statements but lack adaptability for these
two cases, i.e. the neural network trained for the completion of interior
masked images does not generalize well for extrapolating over the boundaries
and vice-versa. In this paper, we present a technique to train both completion
and extrapolation networks concurrently while benefiting each other. We
demonstrate our method's efficiency in completing large missing regions and we
show the comparisons with the contemporary state of the art baseline.
