Ambiguous Data

Ambiguous data, where correct labeling is subjective or uncertain, poses a significant challenge across diverse machine learning applications, from image classification to natural language processing. Current research focuses on developing methods to quantify and mitigate the impact of this ambiguity, including techniques like post-hoc uncertainty estimation for existing models and novel training strategies that incorporate label uncertainty or ambiguity directly. These advancements aim to improve model robustness, accuracy, and efficiency by addressing the "garbage in, garbage out" problem inherent in using noisy or imprecisely labeled datasets, ultimately leading to more reliable and trustworthy AI systems.