Abstract
The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion). Accordingly, we present a deep learning approach based on Iizuka et al. for adversarially training a network to hallucinate past image boundaries. We use a three-phase training schedule to stably train a DCGAN architecture on a subset of the Places365 dataset. In line with Iizuka et al., we also use local discriminators to enhance the quality of our output. Once trained, our model is able to outpaint $128 \times 128$ color images relatively realistically, thus allowing for recursive outpainting. Our results show that deep learning approaches to image outpainting are both feasible and promising.
Abstract (translated)
图像修复（外推）的挑战性任务相对于其表兄，图像修复（完成）而受到的关注相对较少。因此，我们提出了一种基于Iizuka等人的深度学习方法。用于对网络进行异常训练以产生过去图像边界的幻觉。我们使用三阶段培训计划在Places365数据集的子集上稳定地训练DCGAN架构。与Iizuka等人一致，我们还使用局部鉴别器来提高输出质量。经过训练，我们的模型能够相对逼真地显示128美元以上的128美元彩色图像，从而实现递归式修复。我们的研究结果表明，图像修复的深度学习方法既可行又有前途。
URL
https://arxiv.org/abs/1808.08483
