  In this paper, we study the problem of generating a set ofrealistic and
diverse backgrounds when given only a smallforeground region. We refer to this
task as image outpaint-ing. The technical challenge of this task is to
synthesize notonly plausible but also diverse image outputs.
Traditionalgenerative adversarial networks suffer from mode collapse.While
recent approaches propose to maximize orpreserve the pairwise distance between
generated sampleswith respect to their latent distance, they do not
explicitlyprevent the diverse samples of different conditional inputsfrom
collapsing. Therefore, we propose a new regulariza-tion method to encourage
diverse sampling in conditionalsynthesis. In addition, we propose a feature
pyramid dis-criminator to improve the image quality. Our experimen-tal results
show that our model can produce more diverseimages without sacrificing visual
quality compared to state-of-the-arts approaches in both the CelebA face
dataset and the Cityscape scene dataset.
