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[D] Models which try to learn operations like mirroring


Some time back someone jested on this sub about swapping a pair of images (x, y = y, x) using a ‘model’ (I believe the context was a faceswap news) and I was wondering whether there are works that try to learn discrete-ish operations like swap, mirror etc. using a neural network (possibly just to poke around the ‘learnings’). For example, a network that learns image mirroring horizontally (third panel is prediction from a bad feed forward model trying to minimize mse on the flipped image).

Considering that data augmentation in most fields use a lot of similar operations, I tried looking around there but didn’t find anything (not sure what to search for exactly). A recent one does some sort of learned data augmentation but its output is based on these operations and not discovering them. I guess the problem doesn’t make much sense in context of models like neural nets and fits in better with a more GOFAI approach where we impose semantics and then extract rules from data. In any case, wanted to know sources that have inspected this or similar ideas.

submitted by /u/gwynbleiddeyr
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.