Pseudo Labeled

Pseudo-labeling leverages unlabeled data by assigning predicted labels (pseudo-labels) to train or improve machine learning models, addressing the scarcity of labeled data in many applications. Current research focuses on refining pseudo-label generation techniques, often incorporating strategies like in-context learning with large language models or contrastive learning, and developing robust methods to handle noisy pseudo-labels, for example through uncertainty estimation or bi-level optimization. This approach is proving valuable across diverse fields, from improving text classification and virtual try-on to enhancing robot localization and combating online human trafficking by enabling the training of effective models with limited labeled examples.