Deep imbalanced regression

Deep imbalanced regression refers to the situation of handling regression problems under a deep learning framework when the data distribution is extremely unbalanced. Its goal is to improve the prediction accuracy for minority class samples and ensure that the model can generalize effectively across different data density regions through optimization algorithms and model structures. This technique has significant application value in fields such as financial risk assessment, medical diagnosis, and environmental monitoring, enabling more accurate identification and prediction of rare but critical events.