Transductive Inference

Transductive inference is a machine learning paradigm that leverages unlabeled data within a specific dataset to improve model performance, contrasting with inductive inference which generalizes to unseen data. Current research focuses on applying transductive methods to enhance various tasks, including few-shot and zero-shot learning, often employing techniques like prototype refinement, expectation-maximization algorithms, and hybrid models combining eidetic and fading memory. This approach shows promise in improving accuracy and robustness across diverse applications, from remote sensing and medical image analysis to natural language processing and bioacoustic event detection, particularly where labeled data is scarce.