  Despite its remarkable empirical success as a highly competitive branch of
artificial intelligence, deep learning is often blamed for its widely known low
interpretation and lack of firm and rigorous mathematical foundation. However,
most theoretical endeavor is devoted in discriminative deep learning case,
whose complementary part is generative deep learning. To the best of our
knowledge, we firstly highlight landscape of empirical error in generative case
to complete the full picture through exquisite design of image super resolution
under norm based capacity control. Our theoretical advance in interpretation of
the training dynamic is achieved from both mathematical and biological sides.
