title: Is there a theoritical explanation/quantification for deeper CNN layers learning more sophisticated features?
body: <p>I have seen the claim that deeper layers in CNN learn to recognize more sophisticated features. This usually comes with a picture of earlier filters recognizing lines/simple curves, and latter filters recognizing more complicated patterns. It makes intuitive sense: the further you are from the data, the more abstract your understanding of it. Is there a theoritical explanation for this, though?</p>