Novel Regularization

Novel regularization techniques in machine learning aim to improve model performance, stability, and generalization by adding constraints to the training process. Current research focuses on developing regularization methods tailored to specific challenges, such as limited data in GANs (using multi-scale structural self-dissimilarity), preserving geometric structures in hyperbolic neural networks (via Gromov-Wasserstein distance), and mitigating catastrophic forgetting in continual learning (through centroid matching). These advancements enhance model robustness, efficiency (e.g., by reducing network depth), and fairness, leading to improved accuracy and applicability across diverse domains including medical imaging and EEG analysis.