  Class-wise characteristics of training examples affect the performance of
deep classifiers. A well-studied example is when the number of training
examples of classes follows a long-tailed distribution, a situation that is
likely to yield sub-optimal performance for under-represented classes. This
class imbalance problem is conventionally addressed by approaches relying on
the class-wise cardinality of training examples, such as data resampling. In
this paper, we demonstrate that considering solely the cardinality of classes
does not cover all issues causing class imbalance. To measure class imbalance,
we propose "Class Uncertainty" as the average predictive uncertainty of the
training examples, and we show that this novel measure captures the differences
across classes better than cardinality. We also curate SVCI-20 as a novel
dataset in which the classes have equal number of training examples but they
differ in terms of their hardness; thereby causing a type of class imbalance
which cannot be addressed by the approaches relying on cardinality. We
incorporate our "Class Uncertainty" measure into a diverse set of ten class
imbalance mitigation methods to demonstrate its effectiveness on long-tailed
datasets as well as on our SVCI-20. Code and datasets will be made available.
