  In this paper, we propose a novel model-parallel learning method, called
local critic training, which trains neural networks using additional modules
called local critic networks. The main network is divided into several layer
groups and each layer group is updated through error gradients estimated by the
corresponding local critic network. We show that the proposed approach
successfully decouples the update process of the layer groups for both
convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In
addition, we demonstrate that the proposed method is guaranteed to converge to
a critical point. We also show that trained networks by the proposed method can
be used for structural optimization. Experimental results show that our method
achieves satisfactory performance, reduces training time greatly, and decreases
memory consumption per machine. Code is available at
https://github.com/hjdw2/Local-critic-training.
