  How to understand deep learning systems remains an open problem. In this
paper we propose that the answer may lie in the geometrization of deep
networks. Geometrization is a bridge to connect physics, geometry, deep network
and quantum computation and this may result in a new scheme to reveal the rule
of the physical world. By comparing the geometry of image matching and deep
networks, we show that geometrization of deep networks can be used to
understand existing deep learning systems and it may also help to solve the
interpretability problem of deep learning systems.
