Pseudo-labeling is a semi-supervised learning technique where a model generates 'pseudo-labels' for unlabeled data, which are then used as if they were true labels to train the model further. This method leverages large amounts of unannotated data to improve model performance, especially in low-data scenarios.
Pseudo-labeling is a smart way to train AI models when you don't have much labeled data. It works by having the model predict labels for unclassified data, then using those predictions as if they were real labels to learn more. This helps the model get better without needing expensive human annotation for every piece of data.
proxy labels, self-training, SSL
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