Multi Threshold

Multi-thresholding is a technique enhancing the capabilities of various neural network architectures, primarily spiking neural networks (SNNs) and selective classification systems, by incorporating multiple thresholds for signal processing or decision-making. Current research focuses on improving the performance and efficiency of these systems through novel multi-threshold models, such as learnable multi-hierarchical threshold models, and developing appropriate evaluation metrics like the Area under the Generalized Risk Coverage curve (AUGRC). This approach shows promise in improving the accuracy and energy efficiency of SNNs, making them competitive with traditional artificial neural networks, and also in creating more reliable machine learning systems for applications like real-time object detection and clinical diagnostics.