Class Imbalance

1. re-sampling
2. synthetic samples: generate more samples for minor classes
3. re-weighting
4. few-shot learning
5. decoupling representation and classifier learning: use normal sampling in the feature learning stage and use re-sampling in the classifier learning stage.