Correlation Loss

Correlation loss is a machine learning technique that focuses on optimizing the relationship between different prediction components within a model, rather than solely minimizing individual prediction errors. Current research explores its application in diverse areas, including image registration, object detection, and regression tasks, often employing convolutional neural networks (CNNs) and transformers to improve feature extraction and model performance. By directly optimizing correlation between outputs, such as classification and localization scores or regression predictions, this approach aims to improve model accuracy and robustness, leading to advancements in various computer vision and medical image analysis applications.