Long Tailed Problem

The "long-tailed problem" in machine learning describes the challenge of training models on datasets where class frequencies are highly skewed, with a few dominant classes ("head") and many under-represented classes ("tail"). Current research focuses on mitigating the bias towards head classes through techniques like asymmetric loss functions, data augmentation (e.g., using GANs or diffusion models), and gradient adjustment methods, often applied within various model architectures including graph neural networks and sequential recommender systems. Addressing this problem is crucial for improving the performance and reliability of machine learning models in real-world applications, particularly in domains like healthcare and recommendation systems where imbalanced data is common.