Label Uncertainty

Label uncertainty, the inherent ambiguity or disagreement in assigning labels to data, is a critical challenge across various machine learning domains. Current research focuses on developing methods to incorporate this uncertainty into model training, leveraging techniques like soft labels, Bayesian neural networks, and uncertainty-aware loss functions, often within frameworks such as Multiple Instance Learning or ranking-based approaches. Addressing label uncertainty improves model robustness, calibration, and generalizability, leading to more reliable predictions, particularly in applications with limited or noisy annotations, such as medical image analysis and remote sensing. This work has significant implications for improving the accuracy and trustworthiness of machine learning models across diverse fields.