This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.
We propose a new learning algorithm of deep neural networks, which unlocks the layer-wise dependency of backpropagation. An alternative training paradigm for DNIs in which the auxiliary module is trained to approximate directly the final output of the original model, offering side benefits. Describes a method of training neural networks without update locking.