Iterative Pseudo Labeling

Iterative pseudo-labeling (IPL) is a semi-supervised learning technique that iteratively refines model predictions on unlabeled data to improve model performance. Current research focuses on applying IPL across diverse domains, including speaker recognition, object detection, and anomaly detection, often integrating it with techniques like label smoothing, distributionally robust optimization, and various model architectures such as transformers and neural transducers. The effectiveness of IPL in boosting performance with limited labeled data makes it a valuable tool for resource-constrained scenarios and significantly impacts fields where acquiring labeled data is expensive or difficult.