  Deep neural networks trained over large datasets learn features that are both
generic to the whole dataset, and specific to individual classes in the
dataset. Learned features tend towards generic in the lower layers and specific
in the higher layers of a network. Methods like fine-tuning are made possible
because of the ability for one filter to apply to multiple target classes. Much
like the human brain this behavior, can also be used to cluster and separate
classes. However, to the best of our knowledge there is no metric for how
applicable learned features are to specific classes. In this paper we propose a
definition and metric for measuring the applicability of learned features to
individual classes, and use this applicability metric to estimate input
applicability and produce a new method of unsupervised learning we call the
CactusNet.
