Missing Label

Missing label problems in machine learning address the challenge of training models with incomplete or inaccurate label information, hindering accurate model learning and evaluation. Current research focuses on developing robust algorithms and model architectures, such as those incorporating positive-unlabeled learning, metric learning, and contrastive learning, to mitigate the impact of missing labels across various tasks including extreme classification, image recognition, and sound event detection. These advancements are crucial for improving the reliability and performance of machine learning models in real-world applications where fully labeled datasets are often unavailable or prohibitively expensive to obtain, impacting fields ranging from search engines to medical diagnosis.