Partial Label

Partial label learning (PLL) tackles the challenge of training machine learning models with incomplete or ambiguous labels, where each data point is associated 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 convolutional neural networks (CNNs) and recurrent neural networks (RNNs), along with algorithms incorporating techniques such as pseudo-labeling, label smoothing, and optimal transport. The significance of PLL lies in its ability to reduce the high cost and effort of obtaining fully labeled datasets, particularly in domains like medical image analysis and multi-label classification, where complete annotation is often impractical. This makes PLL a crucial area for advancing machine learning's applicability to real-world problems with limited labeled data.