  Object frequency in the real world often follows a power law, leading to a
mismatch between datasets with long-tailed class distributions seen by a
machine learning model and our expectation of the model to perform well on all
classes. We analyze this mismatch from a domain adaptation point of view. First
of all, we connect existing class-balanced methods for long-tailed
classification to target shift, a well-studied scenario in domain adaptation.
The connection reveals that these methods implicitly assume that the training
data and test data share the same class-conditioned distribution, which does
not hold in general and especially for the tail classes. While a head class
could contain abundant and diverse training examples that well represent the
expected data at inference time, the tail classes are often short of
representative training data. To this end, we propose to augment the classic
class-balanced learning by explicitly estimating the differences between the
class-conditioned distributions with a meta-learning approach. We validate our
approach with six benchmark datasets and three loss functions.
