Along the advanced progresses on deep neural networks, there are many impressive results on image inpainting. Consequently, several research are trying to transfer successful experiences into image outpainting. Contextual attention net is one of the popular architectural units being applied to outpainting. We argue that it may not as suitable when embedded in an outpainting network. Instead, we adopt SEnet for it has global receptive field and channel-wise feature recalibration. This is very helpful for image outpainting. We also propose a local discriminator mechanism to decide whether a randomly select partial image is a real one. By ‘randomness’, the generator can produce a realistic result.
