Naive Ensemble

Naive ensembles, which involve averaging predictions from multiple independently trained models, are a fundamental technique in machine learning aiming to improve model performance and robustness. Current research focuses on improving the efficiency of naive ensembles, exploring strategies like weight averaging and adaptive model selection to reduce computational costs without sacrificing accuracy, particularly in applications like training data attribution and continual learning. These advancements are significant because they enhance the practicality and scalability of ensemble methods across various domains, including image classification, semantic segmentation, and outlier detection, ultimately leading to more efficient and effective machine learning models.