Candidate Label

Candidate label learning addresses the challenge of training machine learning models when each data point is associated with a set of potential labels, only one of which is correct. Current research focuses on developing algorithms that effectively disambiguate these candidate labels, often leveraging techniques like prompt engineering, optimal transport, and complementary classifiers to improve accuracy and robustness, particularly in scenarios with imbalanced data or noisy labels. This field is significant because it enables the use of datasets with inherently ambiguous or incomplete annotations, expanding the applicability of machine learning to real-world problems where obtaining perfectly labeled data is difficult or expensive.