Label Disambiguation

Label disambiguation tackles the challenge of learning from data where each instance is associated with a set of possible labels, rather than a single true label. Current research focuses on developing robust algorithms, often incorporating contrastive learning and prototype-based methods, to effectively disambiguate these candidate labels and improve model accuracy, particularly in scenarios with noisy or imbalanced data. These advancements are significant because they enable more efficient and reliable machine learning from weakly supervised data, impacting various applications including image recognition, natural language processing, and robotics. The development of adaptive strategies that handle both label ambiguity and data imbalances is a key area of ongoing investigation.