CNN representation of objects is nothing more than a series of filters. The criticism being addressed here is to create an architecture representing the actual

CNN representation of objects is nothing more than a series of filters. The criticism being addressed here is to create an architecture representing the actual geometry of objects. Consider this problem: given a picture of a simple object, a human could draw a picture of the same object rotated at some angle. Currently there is no elegant NN solution to this because no architectures "understand" a three dimensional representation from images. A CNN can identify every video frame of a dog running as a dog, but there is no conception of the same dog running through space.