Pseudo Labeled Data

Pseudo-labeling leverages unlabeled data by assigning labels predicted by a model, thereby augmenting training datasets and improving model performance, particularly in scenarios with limited labeled data. Current research focuses on improving the accuracy and reliability of these pseudo-labels through techniques like uncertainty estimation, ensemble methods, and refined selection criteria, often integrated with self-training or semi-supervised learning frameworks. This approach holds significant value across diverse fields, enhancing model training in applications ranging from speech recognition and natural language processing to image classification and time-series analysis where acquiring labeled data is expensive or difficult.