  Class imbalance is a pervasive issue among classification models including
deep learning, whose capacity to extract task-specific features is affected in
imbalanced settings. However, the challenges of handling imbalance among a
large number of classes, commonly addressed by deep learning, have not received
a significant amount of attention in previous studies. In this paper, we
propose an extension of the deep over-sampling framework, to exploit
automatically-generated abstract-labels, i.e., a type of side-information used
in weak-label learning, to enhance deep representation learning against class
imbalance. We attempt to exploit the labels to guide the deep representation of
instances towards different subspaces, to induce a soft-separation of inherent
subtasks of the classification problem. Our empirical study shows that the
proposed framework achieves a substantial improvement on image classification
benchmarks with imbalanced among large and small numbers of classes.
