  Real-world data often follow a long-tailed distribution as the frequency of
each class is typically different. For example, a dataset can have a large
number of under-represented classes and a few classes with more than sufficient
data. However, a model to represent the dataset is usually expected to have
reasonably homogeneous performances across classes. Introducing class-balanced
loss and advanced methods on data re-sampling and augmentation are among the
best practices to alleviate the data imbalance problem. However, the other part
of the problem about the under-represented classes will have to rely on
additional knowledge to recover the missing information.
  In this work, we present a novel approach to address the long-tailed problem
by augmenting the under-represented classes in the feature space with the
features learned from the classes with ample samples. In particular, we
decompose the features of each class into a class-generic component and a
class-specific component using class activation maps. Novel samples of
under-represented classes are then generated on the fly during training stages
by fusing the class-specific features from the under-represented classes with
the class-generic features from confusing classes. Our results on different
datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version
of CIFAR have shown the state of the art performances.
