Invariant learning of multilayer networks for generalization

The purpose of this paper is to improve generalization ability of multilayer neural networks. Our approach is to construct a neural network whose outputs are invariant with respect to some transformations of input patterns. We present an error function to incorporate invariances into a neural network through training. A special case of the proposed method can be considered as tangent prop algorithm. Finally, we show some experimental results to demonstrate the effectiveness of the proposed method.