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

arXiv: https://arxiv.org/abs/1810.01622 · HTML: https://mlsanity.com/paper/1810.01622/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [ETO Meets Scheduling: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem](https://mlsanity.com/paper/2206.12902/) (2022)

## Abstract (arXiv)

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.