Regression via Classification

Regression via classification (RvC) tackles regression problems by framing them as classification tasks, offering advantages in handling complex output distributions and improving model robustness. Current research focuses on developing effective RvC methods for various applications, including improving data augmentation techniques for regression and exploring hybrid classification-regression models to leverage the strengths of both approaches. This approach shows promise in diverse fields, from improving the accuracy of scene reconstruction and seismic intensity prediction to enhancing the efficiency of active learning in resource-constrained settings. The resulting improvements in model accuracy, stability, and efficiency are significant for both scientific understanding and practical applications.