Gating improves neural network performance

Our first purpose is to study the performance of gating network functions in a committee machine setting. The problem of image deblurring is used to test the capability of such a system. Input clustering divides the task of deblurring into several subtasks. Each subtask is performed by a projection pursuit learning network (Basu and Su, 1999). We use a dynamic gating structure to combine outputs from various committee members. Our second purpose is to study the possibility of extending the role of the input signal beyond the decision making stage in the gating structure. Input data contain crucial structural information and characteristics of the data in a degraded form. The novel aspect of this work is the use of input signal with the output from the gating structure to produce the overall output. Resulting images show significant improvement over images that are produced from the output of the gating structure alone