Structural Regularization

Structural regularization in machine learning focuses on imposing constraints on model parameters or learned representations to improve generalization, robustness, and interpretability. Current research explores diverse applications, including image processing (e.g., low-light enhancement, compression, and medical image reconstruction), natural language processing, and adversarial attacks, employing techniques like probabilistic coding, group sparsity regularization, and equivariance constraints within various model architectures (e.g., neural networks, diffusion models). These advancements enhance model performance and provide insights into the underlying data structure, leading to more reliable and explainable AI systems across multiple domains.