MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
  Class-imbalanced data, in which some classes contain far more samples than
others, is ubiquitous in real-world applications. Standard techniques for
handling class-imbalance usually work by training on a re-weighted loss or on
re-balanced data. Unfortunately, training overparameterized neural networks on
such objectives causes rapid memorization of minority class data. To avoid this
trap, we harness meta-learning, which uses both an ''outer-loop'' and an
''inner-loop'' loss, each of which may be balanced using different strategies.
We evaluate our method, MetaBalance, on image classification, credit-card fraud
detection, loan default prediction, and facial recognition tasks with severely
imbalanced data, and we find that MetaBalance outperforms a wide array of
popular re-sampling strategies.
