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Despite its remarkable empirical success as a highly competitive branch ofartificial intelligence, deep learning is often blamed for its widely known lowinterpretation 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 ourknowledge, we firstly highlight landscape of empirical error in generative caseto complete the full picture through exquisite design of image super resolutionunder norm based capacity control. Our theoretical advance in interpretation ofthe training dynamic is achieved from both mathematical and biological sides.

Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control