Contrastive Regression

Contrastive regression is a machine learning technique that leverages the power of contrastive learning to improve the accuracy and robustness of regression models. Current research focuses on applying this approach to diverse tasks, including image quality assessment, action quality assessment, and neurocognitive prediction, often employing variations of supervised contrastive learning and incorporating strategies like mixup or patch sampling to enhance performance. This methodology shows promise in addressing challenges like limited labeled data, inconsistent annotations from multiple sources, and the need for more continuous and ordered representations in regression tasks, ultimately leading to improved model generalization and accuracy across various domains.