Long Tailed Data

Long-tailed data, characterized by a highly skewed class distribution with a few dominant classes and many under-represented ones, poses a significant challenge for machine learning models prone to bias towards majority classes. Current research focuses on developing robust algorithms and model architectures, such as those based on generative adversarial networks (GANs), transformers, and federated learning, to address this imbalance and improve the classification accuracy of minority classes. These efforts are crucial for improving the reliability and fairness of machine learning systems across diverse real-world applications where imbalanced data is prevalent, impacting fields ranging from medical diagnosis to object recognition.