Long-tail learning is a research domain focused on developing machine learning models that perform effectively on datasets characterized by a highly skewed distribution of classes or features, where a few instances are frequent and a large number are rare. This challenge arises from the 'long tail' phenomenon in data distributions, where standard learning algorithms often exhibit poor performance on the infrequent categories due to insufficient training data. The field investigates specialized techniques to mitigate this imbalance, aiming to improve model generalization and predictive accuracy across the entire distribution, particularly for the minority classes, which are often crucial in real-world applications.