Long Tailed

Long-tailed learning addresses the challenge of training machine learning models on datasets where class frequencies are highly imbalanced, with a few dominant classes and many under-represented classes. Current research focuses on developing novel loss functions, leveraging generative models to augment data, and employing techniques like contrastive learning and knowledge distillation to improve the representation and classification of minority classes, often within the context of vision transformers and convolutional neural networks. This field is crucial for improving the robustness and generalizability of AI systems in real-world applications where data naturally exhibits long-tailed distributions, such as medical image analysis and object detection.