  Recently, the problem of local minima in very high dimensional non-convex
optimization has been challenged and the problem of saddle points has been
introduced. This paper introduces a dynamic type of normalization that forces
the system to escape saddle points. Unlike other saddle point escaping
algorithms, second order information is not utilized, and the system can be
trained with an arbitrary gradient descent learner. The system drastically
improves learning in a range of deep neural networks on various data-sets in
comparison to non-CPN neural networks.
