  Although humans perform well at predicting what exists beyond the boundaries
of an image, deep models struggle to understand context and extrapolation
through retained information. This task is known as image outpainting and
involves generating realistic expansions of an image's boundaries. Current
models use generative adversarial networks to generate results which lack
localized image feature consistency and appear fake. We propose two methods to
improve this issue: the use of a local and global discriminator, and the
addition of residual blocks within the encoding section of the network.
Comparisons of our model and the baseline's L1 loss, mean squared error (MSE)
loss, and qualitative differences reveal our model is able to naturally extend
object boundaries and produce more internally consistent images compared to
current methods but produces lower fidelity images.
