Adapting Classifiers To Changing Class Priors During Deployment
Natnael Daba Bruce McIntosh Abhijit Mahalanobis
Conventional classifiers are trained and evaluated using balanced data sets
in which all classes are equally present. Classifiers are now trained on large
data sets such as ImageNet, and are now able to classify hundreds (if not
thousands) of different classes. On one hand, it is desirable to train such
general-purpose classifier on a very large number of classes so that it
performs well regardless of the settings in which it is deployed. On the other
hand, it is unlikely that all classes known to the classifier will occur in
every deployment scenario, or that they will occur with the same prior
probability. In reality, only a relatively small subset of the known classes
may be present in a particular setting or environment. For example, a
classifier will encounter mostly animals if its deployed in a zoo or for
monitoring wildlife, aircraft and service vehicles at an airport, or various
types of automobiles and commercial vehicles if it is used for monitoring
traffic. Furthermore, the exact class priors are generally unknown and can vary
over time. In this paper, we explore different methods for estimating the class
priors based on the output of the classifier itself. We then show that
incorporating the estimated class priors in the overall decision scheme enables
the classifier to increase its run-time accuracy in the context of its
deployment scenario.