  This study addresses an issue of co-adaptation between a feature extractor
and a classifier in a neural network. A naive joint optimization of a feature
extractor and a classifier often brings situations in which an excessively
complex feature distribution adapted to a very specific classifier degrades the
test performance. We introduce a method called Feature-extractor Optimization
through Classifier Anonymization (FOCA), which is designed to avoid an explicit
co-adaptation between a feature extractor and a particular classifier by using
many randomly-generated, weak classifiers during optimization. We put forth a
mathematical proposition that states the FOCA features form a point-like
distribution within the same class in a class-separable fashion under special
conditions. Real-data experiments under more general conditions provide
supportive evidences.
