Partial Label Learning

Partial label learning (PLL) addresses the challenge of training machine learning models using data where each example is annotated with a set of possible labels, only one of which is correct. Current research focuses on improving label disambiguation techniques, often employing deep learning architectures like neural networks and Gaussian processes, and exploring variations such as multi-instance PLL and handling noisy or unreliable labels. These advancements are significant because they enable more efficient and robust model training with less precise annotations, impacting various fields including image classification, natural language processing, and biomedical applications where obtaining perfectly labeled data is difficult or expensive. Furthermore, research is actively addressing issues like class imbalance and out-of-distribution data within the PLL framework.