Representation Calibration

Representation calibration aims to improve the reliability and generalizability of learned representations by addressing issues like noisy labels, class imbalance, and domain shifts. Current research focuses on developing methods that calibrate representations across different views or domains, often employing techniques like contrastive learning, Gaussian distribution modeling, or novel normalization strategies within model architectures such as neural radiance fields or classifiers based on von Mises-Fisher distributions. These advancements are significant for improving the performance and robustness of machine learning models in various applications, including image classification, object detection, and visual question answering, where data quality and generalizability are critical concerns.