  Class imbalance is a problem of significant importance in applied deep
learning where trained models are exploited for decision support and automated
decisions in critical areas such as health and medicine, transportation, and
finance. The challenge of learning deep models from imbalanced training data
remains high, and the state-of-the-art solutions are typically data dependent
and primarily focused on image data. Real-world imbalanced classification
problems, however, are much more diverse thus necessitating a general solution
that can be applied to tabular, image and text data. In this paper, we propose
ReMix, a training technique that leverages batch resampling, instance mixing
and soft-labels to enable the induction of robust deep models for imbalanced
learning. Our results show that dense nets and CNNs trained with ReMix
generally outperform the alternatives according to the g-mean and are better
calibrated according to the balanced Brier score.
