Long Tailed Distribution

Long-tailed distributions, where a few classes dominate while many others have scarce data, pose a significant challenge for machine learning models, hindering their ability to accurately classify less frequent classes. Current research focuses on developing techniques to address this imbalance, including data augmentation, loss function modifications (e.g., balanced softmax), and novel training strategies that prioritize learning from tail classes, often incorporating elements of contrastive learning, optimal transport, or uncertainty calibration. These advancements are crucial for improving the performance of machine learning models in real-world applications where data is inherently imbalanced, impacting fields such as image recognition, natural language processing, and recommendation systems.