论文标题
使用空间随机网络的低失真块逆转采样
Low Distortion Block-Resampling with Spatially Stochastic Networks
论文作者
论文摘要
我们将来自尽可能多样化的旧图像产生新图像的新图像的问题正式化和攻击，只允许它们改变图像的某些部分而无需限制，同时保持全球一致。这涵盖了生成建模中发现的典型情况，我们对生成的数据的一部分感到满意，但想重新采样其他人（“我整体上喜欢这座生成的城堡，但是这座塔看起来不现实，我想要一个新的塔”）。为了攻击这个问题，我们从最佳条件和无条件生成模型构建，以根据需要引入新的网络体系结构，培训过程和算法，以根据需要重新采样部分。
We formalize and attack the problem of generating new images from old ones that are as diverse as possible, only allowing them to change without restrictions in certain parts of the image while remaining globally consistent. This encompasses the typical situation found in generative modelling, where we are happy with parts of the generated data, but would like to resample others ("I like this generated castle overall, but this tower looks unrealistic, I would like a new one"). In order to attack this problem we build from the best conditional and unconditional generative models to introduce a new network architecture, training procedure, and algorithm for resampling parts of the image as desired.
