Hi,

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.