Regularization Approach

Regularization techniques in machine learning aim to prevent overfitting and improve the generalization ability of models by constraining their complexity. Current research focuses on developing and combining various regularization approaches, including those based on weight decay, data augmentation, and physically-inspired constraints, across diverse model architectures such as neural radiance fields (NeRFs), gradient-boosted decision trees (GBDTs), and convolutional neural networks (CNNs). These advancements enhance model robustness, particularly in scenarios with limited data or significant domain shifts, leading to improved performance and reliability in various applications, including image processing, 3D reconstruction, and medical image analysis.