Self Recalibration

Self-recalibration focuses on improving the accuracy and reliability of machine learning models by adjusting their confidence estimates or internal parameters to account for various sources of error, such as model biases, noisy data, or changing environmental conditions. Current research explores diverse recalibration methods, including those based on adjusting model weights (e.g., modifying the final layer's geometry), employing recalibration maps, and leveraging few-shot learning techniques to adapt to specific data subsets. These advancements are crucial for enhancing the trustworthiness and robustness of AI systems across diverse applications, from healthcare and autonomous driving to brain-computer interfaces, where reliable performance over time is paramount.