Deep Learning to estimate what is beyond the edge I have an image of some data which is approximately 4,000 x 8,000 pixels.  I am interested in finding out if anyone has used a deep learning algorithm to predict what would be on the image if it extended 100 more pixels in each direction.  I would imagine that the data could be trained on smaller rectangles, and then the rules developed would be used to extend beyond the image given.  Has anyone seen a problem like this (and is there a reference)?   Even if not, what deep learning scheme would be best for this?
I think the closest problem that has been addressed with deep learning is image inpainting, that is, filling a blacked out region in the image:



For instance, this paper: Semantic Image Inpainting with Perceptual and Contextual Losses.

So it is certainly possible to fill missing information from an image with deep learning.