We propose a generative model that can infer a distribution for the underlying spatial signal conditioned on sparse samples e.g. plausible images given a few observed pixels. In contrast to sequential autoregressive generative models, our model allows conditioning on arbitrary samples and can answer distributional queries for any location. We empirically validate our approach across three image datasets and show that we learn to generate diverse and meaningful samples, with the distribution variance reducing given more observed pixels. We also show that our approach is applicable beyond images and can allow generating other types of spatial outputs e.g. polynomials, 3D shapes, and videos.
翻译:我们提出了一个基因模型,可以推断以稀有样本为条件的基本空间信号的分布,例如,以少数观测到的像素提供的貌似图像。与相继的自动递减基因模型相比,我们的模型允许对任意样本进行调节,并可以回答任何地点的分布查询。我们通过经验验证了我们通过三个图像数据集的方法,并表明我们学会生成多样和有意义的样本,而分布差异则随着更多观测到的像素而缩小。我们还表明,我们的方法超出了图像的范围,可以产生其他类型的空间输出,例如多面形、3D形状和视频。