  We investigate the problem of training neural networks from incomplete images
without replacing missing values. For this purpose, we first represent an image
as a graph, in which missing pixels are entirely ignored. The graph image
representation is processed using a spatial graph convolutional network (SGCN)
-- a type of graph convolutional networks, which is a proper generalization of
classical CNNs operating on images. On one hand, our approach avoids the
problem of missing data imputation while, on the other hand, there is a natural
correspondence between CNNs and SGCN. Experiments confirm that our approach
performs better than analogical CNNs with the imputation of missing values on
typical classification and reconstruction tasks.
