Copula Enhanced Convolutional Neural Network

Copula-enhanced convolutional neural networks (CNNs) integrate copula functions, which model the dependence structure between variables, into CNN architectures to improve the accuracy and interpretability of predictions, particularly in multi-output regression tasks. Current research focuses on applying these models to diverse fields, including medical image analysis (e.g., myopia screening) and remote sensing (e.g., change detection), leveraging various CNN backbones and incorporating copula-based loss functions to capture complex relationships between outputs. This approach offers advantages over traditional methods by jointly modeling multiple correlated outputs and enhancing the explainability of deep learning models, leading to more robust and reliable predictions in various applications.