Adaptive Threshold

Adaptive thresholding is a technique used across diverse fields to dynamically adjust decision boundaries based on data characteristics or system dynamics, aiming to improve performance and robustness. Current research focuses on applications in image processing (denoising, segmentation, and feature extraction), neural network training (especially spiking neural networks and deep learning models), and distributed machine learning, employing various methods including fuzzy logic, neural network architectures (e.g., encoder-decoder networks), and novel optimization algorithms. These advancements enhance the efficiency and accuracy of various applications, from medical image analysis and anomaly detection to improving the fairness and robustness of machine learning models.