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Learning from imbalanced data is one of the most significant challenges inreal-world classification tasks. In such cases, neural networks performance issubstantially impaired due to preference towards the majority class. Existingapproaches attempt to eliminate the bias through data re-sampling orre-weighting the loss in the learning process. Still, these methods tend tooverfit the minority samples and perform poorly when the structure of theminority class is highly irregular. Here, we propose a novel deep meta-learningtechnique to augment a given imbalanced dataset with new minority instances.These additional data are incorporated in the classifier's deep-learningprocess, and their contributions are learned explicitly. The advantage of theproposed method is demonstrated on synthetic and real-world datasets withvarious imbalance ratios.

Imbalanced Classification via Explicit Gradient Learning From Augmented Data