Threshold Based Auto Labeling

Threshold-based auto-labeling (TBAL) aims to efficiently create large labeled datasets by automatically labeling data points based on a model's confidence scores exceeding a predefined threshold. Current research focuses on optimizing the threshold selection process, developing improved confidence functions to mitigate model overconfidence, and analyzing the sample complexity required for reliable auto-labeling. This approach significantly reduces the need for manual labeling, impacting various machine learning applications by accelerating model training and potentially improving data efficiency, but careful consideration of potential pitfalls, such as high validation data requirements, is crucial for successful implementation.