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Class imbalance is a problem of significant importance in applied deeplearning where trained models are exploited for decision support and automateddecisions in critical areas such as health and medicine, transportation, andfinance. The challenge of learning deep models from imbalanced training dataremains high, and the state-of-the-art solutions are typically data dependentand primarily focused on image data. Real-world imbalanced classificationproblems, however, are much more diverse thus necessitating a general solutionthat can be applied to tabular, image and text data. In this paper, we proposeReMix, a training technique that leverages batch resampling, instance mixingand soft-labels to enable the induction of robust deep models for imbalancedlearning. Our results show that dense nets and CNNs trained with ReMixgenerally outperform the alternatives according to the g-mean and are bettercalibrated according to the balanced Brier score.

ReMix: Calibrated Resampling for Class Imbalance in Deep learning