Label Supervision

Label supervision in machine learning focuses on efficiently utilizing limited labeled data to train accurate models, addressing the high cost and effort of manual annotation. Current research explores diverse approaches, including adversarial and probabilistic methods for combining weak supervision signals, developing techniques like decoupled federated learning for privacy-preserving collaboration, and employing pseudo-labeling and self-training to leverage unlabeled data. These advancements are crucial for improving model performance in various applications, particularly where large labeled datasets are unavailable or impractical to obtain, such as in medical image analysis and resource-constrained environments.

Papers