Deep Imbalanced

Deep imbalanced learning addresses the challenge of training effective machine learning models on datasets where classes are represented with vastly unequal frequencies. Current research focuses on improving model generalization to minority classes through techniques like data augmentation (e.g., using generative models or Mixup), modifying loss functions (e.g., employing cost-sensitive learning or distributionally robust optimization), and developing novel sampling strategies. This field is crucial for numerous applications, including medical diagnosis, fraud detection, and AI-aided drug discovery, where imbalanced data is prevalent and accurate prediction on minority classes is vital for reliable and fair outcomes.