Adaptive Thresholding

Adaptive thresholding is a technique used to dynamically adjust decision boundaries in various applications, primarily aiming to improve accuracy and robustness by accounting for data variability and context. Current research focuses on integrating adaptive thresholding into machine learning models, often employing neural networks or other algorithms to learn optimal thresholds based on features like distance, density, or predicted confidence scores. This approach is proving valuable in diverse fields, enhancing performance in tasks such as object detection (3D and image), anomaly detection, medical image segmentation, and speaker verification by reducing false positives and negatives while improving overall classification accuracy.