Label Efficient

Label-efficient learning aims to train accurate machine learning models, particularly deep learning models, using minimal labeled data. Current research focuses on leveraging unlabeled data through techniques like semi-supervised learning, self-supervised learning, active learning, and the incorporation of foundation models and pseudo-labeling strategies, often employing diffusion models, vision transformers, and various attention mechanisms. This field is crucial for addressing the high cost and time constraints associated with data annotation in many domains, including medical image analysis, autonomous driving, and agricultural applications, enabling broader deployment of powerful AI systems.