论文标题
图像完成和推断与上下文周期一致性
Image Completion and Extrapolation with Contextual Cycle Consistency
论文作者
论文摘要
图像完成是指填充图像缺失区域的任务，而图像外推则是指在其边界上扩展图像的任务，同时保持其相干。许多基于GAN的最近作品在解决这些问题陈述方面表现出了进展，但缺乏对这两种情况的适应性，即，训练培训用于完成内部蒙版图像的神经网络并不能很好地推广到外推，而不是范围内的边界和反之亦然。在本文中，我们提出了一种技术，可以同时培训完成和外推网络，同时彼此受益。我们证明了我们的方法在完成大型缺失地区的效率，并展示了与当代最新基线状态的比较。
Image Completion refers to the task of filling in the missing regions of an image and Image Extrapolation refers to the task of extending an image at its boundaries while keeping it coherent. Many recent works based on GAN have shown progress in addressing these problem statements but lack adaptability for these two cases, i.e. the neural network trained for the completion of interior masked images does not generalize well for extrapolating over the boundaries and vice-versa. In this paper, we present a technique to train both completion and extrapolation networks concurrently while benefiting each other. We demonstrate our method's efficiency in completing large missing regions and we show the comparisons with the contemporary state of the art baseline.
