Unlabeled Example

Unlabeled example utilization in machine learning focuses on effectively leveraging abundant unlabeled data to improve model performance, particularly in semi-supervised and positive-unlabeled learning scenarios. Current research emphasizes developing robust methods for selecting informative unlabeled examples, often employing techniques like confidence calibration, contrastive learning, and distance-based pseudo-labeling to mitigate the risks of noisy or imbalanced data. These advancements are crucial for reducing reliance on expensive labeled data, improving model accuracy and efficiency across various applications, including image classification, natural language processing, and object detection.