Adapting Classifiers To Changing Class Priors During Deployment
This addresses the issue of adapting general-purpose classifiers to specific deployment environments with unknown class distributions, which is incremental as it builds on existing classifier frameworks.
The paper tackles the problem of classifiers trained on balanced datasets performing poorly when deployed with unknown and varying class priors, and shows that estimating these priors from classifier outputs and incorporating them into decision-making improves runtime accuracy.
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.