  Different from image inpainting, image outpainting has relative less context
in the image center to capture and more content at the image border to predict.
Therefore, classical encoder-decoder pipeline of existing methods may not
predict the outstretched unknown content perfectly. In this paper, a novel
two-stage siamese adversarial model for image extrapolation, named Siamese
Expansion Network (SiENet) is proposed. In two stages, a novel border sensitive
convolution named adaptive filling convolution is designed for allowing encoder
to predict the unknown content, alleviating the burden of decoder. Besides, to
introduce prior knowledge to network and reinforce the inferring ability of
encoder, siamese adversarial mechanism is designed to enable our network to
model the distribution of covered long range feature for that of uncovered
image feature. The results on four datasets has demonstrated that our method
outperforms existing state-of-the-arts and could produce realistic results.
