I don’t think that’s true. For example, one thing I tried was to use a bottleneck, made of these 2 nodes which I want as the output and force the NN to reconstruct the original image using these 2 numbers. Ideally the “encoder” (going from the image to the 2 numbers) would output some function of x and y and the “decoder” (going from the 2 numbers back to the original image) would learn the inverse of that function of x and y and place the object there. I did that and it works ok-ish, in the sense that for most of the points the ordering is correct, but there are quite a few whose position is pretty messed up. So my point is, this can be done without using labeled examples or non-NN techniques (probably having 10 or 100 times more data would fix the problem even with my approach?). However I was hoping for something more clever that just and encoder-decoder. This is the most obvious thing to try first, but I was hoping (and given the rate at which AI evolves I am pretty sure) that someone has a more effective way of doing this and someone can point me towards that paper/code.