Has there been any work into the effectiveness of teaching ‘rotational extrapolation’ as a mechanism for boosting the effectiveness of image recognition?[D]

Intuitively, one reason it’s easy for people to recognize objects with relatively few samples, is that we have some notion of how to extrapolate the appearance of objects under some non-deformative manipulations. A child can imagine what an object might look like were it to be rotated without having seen it before.

Has there been any work at teaching NN how to ‘imagine’ the appearance of objects subject to these kinds manipulations, as to boost there general effectiveness at image …

image recognition machinelearning teaching work