Transductive Learning

Transductive learning is a machine learning paradigm that leverages both labeled and unlabeled data during the training process to improve model performance and generalization, particularly in scenarios with limited labeled data. Current research focuses on applying transductive methods to various tasks, including few-shot learning, vision-language modeling, and recommender systems, often employing graph-based approaches or optimization strategies to integrate unlabeled data effectively. This approach offers significant advantages in improving model accuracy and efficiency, especially in domains with high data acquisition costs or where generalization to unseen data is crucial, impacting fields like medical image analysis and natural language processing.