Competitive Learning with Feedforward Supervisory Signal for Pre-trained Multilayered Networks

We propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates the classification of a new input with that of pre-trained input. The proposed method effectively uses rich input information in the earlier layer for robust learning and revising of the internal representation in a multilayer neural network.