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Class imbalance is a pervasive issue among classification models includingdeep learning, whose capacity to extract task-specific features is affected inimbalanced settings. However, the challenges of handling imbalance among alarge number of classes, commonly addressed by deep learning, have not receiveda significant amount of attention in previous studies. In this paper, wepropose an extension of the deep over-sampling framework, to exploitautomatically-generated abstract-labels, i.e., a type of side-information usedin weak-label learning, to enhance deep representation learning against classimbalance. We attempt to exploit the labels to guide the deep representation ofinstances towards different subspaces, to induce a soft-separation of inherentsubtasks of the classification problem. Our empirical study shows that theproposed framework achieves a substantial improvement on image classificationbenchmarks with imbalanced among large and small numbers of classes.

Weak-supervision for Deep Representation Learning under Class Imbalance