Transductive Few Shot Learning

Transductive few-shot learning (TFSL) aims to improve the accuracy of machine learning models trained on very limited labeled data by incorporating information from unlabeled data during the learning process. Current research focuses on developing algorithms that effectively leverage this unlabeled data, often employing prototype-based methods, graph-based label propagation, or techniques that address the "hubness" problem inherent in high-dimensional data. These advancements are significant because they enable more accurate and efficient model training in scenarios with scarce labeled data, impacting fields like medical image analysis and other areas where obtaining labeled data is expensive or time-consuming.