  Methods in long-tail learning focus on improving performance for data-poor
(rare) classes; however, performance for such classes remains much lower than
performance for more data-rich (frequent) classes. Analyzing the predictions of
long-tail methods for rare classes reveals that a large number of errors are
due to misclassification of rare items as visually similar frequent classes. To
address this problem, we introduce AlphaNet, a method that can be applied to
existing models, performing post hoc correction on classifiers of rare classes.
Starting with a pre-trained model, we find frequent classes that are closest to
rare classes in the model's representation space and learn weights to update
rare class classifiers with a linear combination of frequent class classifiers.
AlphaNet, applied to several models, greatly improves test accuracy for rare
classes in multiple long-tailed datasets, with very little change to overall
accuracy. Our method also provides a way to control the trade-off between rare
class and overall accuracy, making it practical for long-tail classification in
the wild.
