Ensemble Architecture

Ensemble architectures combine multiple neural networks to improve prediction accuracy, robustness, and uncertainty quantification, surpassing the performance of individual models. Current research focuses on developing efficient ensemble methods, including single-architecture ensembles that integrate multiple models within a single network, and diversity-driven approaches that enhance out-of-distribution detection. These advancements are significant for various applications, such as object detection in autonomous driving and medical image analysis, where reliable predictions and well-calibrated uncertainty estimates are crucial for safety and decision-making. Improved efficiency and robustness are key goals, leading to the exploration of techniques like early-exit strategies and the optimization of ensemble training processes.