Topological Separation versus Weight Sharing in Neural Net Optimization

摘要

Recent advances in neural networks application development for real life problemshave drawn attention to network optimization. Most of the known optimization methods rely heavily on a weight sharing concept for pattern separation and recognition. The shortcoming of the weight sharing method is attributed to a large number of extraneous weights which play a minimal role in pattern separation and recognition. The authors' experiments have shown that up to 97% of the connections in the network can be eliminated with little or no change in the network performance. Topological separation should be used when the size of the network is large enough to tackle real life problems such as fingerprint classification. Their research has focused on the network topology by changing the number of connections as secondary method of optimization. The findings indicate that for large networks, topological separation yields smaller network size that is more suitable for very large scale integration (VLSI) implementation. Topological separation is based on the error surface and information content of the network. As such, it is an economical way of size reduction which leads to overall optimization. The differential pruning of the connections is based on the weight contents rather than number of connections. The training error may vary with the topological dynamics but the correlation between the error surface and recognition rate decreases to a minimum. Topological separation reduces the size of the network by changing its architecture without degrading its performance.