Deep learning is the mainstream technique for many machine learning tasks,
including image recognition, machine translation, speech recognition, and so
on. It has outperformed conventional methods in various fields and achieved
great successes. Unfortunately, the understanding on how it works remains
unclear. It has the central importance to lay down the theoretic foundation for
deep learning.
In this work, we give a geometric view to understand deep learning: we show
that the fundamental principle attributing to the success is the manifold
structure in data, namely natural high dimensional data concentrates close to a
low-dimensional manifold, deep learning learns the manifold and the probability
distribution on it.
We further introduce the concepts of rectified linear complexity for deep
neural network measuring its learning capability, rectified linear complexity
of an embedding manifold describing the difficulty to be learned. Then we show
for any deep neural network with fixed architecture, there exists a manifold
that cannot be learned by the network. Finally, we propose to apply optimal
mass transportation theory to control the probability distribution in the
latent space.

该研究通过几何视角揭示深度学习的关键在于学习数据的流形结构和概率分布，并提出一种控制潜在空间中概率分布的最优质量传递理论。