Early Stopping

Early stopping is a technique used to prevent overfitting in machine learning models by halting training before the model fully converges. Current research focuses on developing more effective early stopping criteria, particularly for noisy data and complex architectures like Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), often employing techniques like loss function analysis, gradient-based metrics, and statistical significance testing. Improved early stopping methods offer significant benefits, including reduced computational costs, enhanced model generalization, and more efficient resource utilization across various applications, from image processing and natural language processing to federated learning and reinforcement learning.