'LARGE CAPACITY NEURAL NETS FOR SCENE ANALYSIS'

摘要

We consider the classification of multiple objects in a scene with distortion and clutter present. Our opinions on the role for neural nets (NNs) in this application and the different properties that NNs must have to address this problem are advanced. A hierarchical/inference approach is suggested using correlation NNs for low-level operations and new classifier NNs with higher-order decision surfaces for the final decision NNs. Our concern is NN capacity and performance (in noise). Our capacity guidelines advanced concern the number of neurons, use of analog neurons, Ho-Kashyap (HK) NNs, and two new NNs with higher-order decision surfaces. Our noise performance guidelines advanced concern the number of neuron layers, hidden-layer neuron encoding, and robust HK NNs.