  We present the clustering learning technique applied to multi-layer
feedforward deep neural networks. We show that this unsupervised learning
technique can compute network filters with only a few minutes and a much
reduced set of parameters. The goal of this paper is to promote the technique
for general-purpose robotic vision systems. We report its use in static image
datasets and object tracking datasets. We show that networks trained with
clustering learning can outperform large networks trained for many hours on
complex datasets.
