| Abstract | Long-tailed distributions and class imbalance are problems of significant importance in applied deep learning where trained models are exploited for decision support and decision automation in critical areas such as health and medicine, transportation and finance. The challenge of learning deep models from such data remains high, and the state-of-the-art solutions are typically data dependent and primarily focused on images. Important real-world problems, however, are much more diverse thus necessitating a general solution that can be applied to diverse data types. In this paper, we propose ReMix, a training technique that seamlessly leverages batch resampling, instance mixing and soft-labels to efficiently enable the induction of robust deep models from imbalanced and long-tailed datasets. Our results show that fully connected neural networks and Convolutional Neural Networks (CNNs) trained with ReMix generally outperform the alternatives according to the g-mean and are better calibrated according to the balanced Brier score. |
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