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Transductive Learning

In this setting, both a labeled training sample and an (unlabeled) test sample are provided at training time. The goal is to predict only the labels of the given test instances as accurately as possible.

Papers

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Showing 41–50 of 135 papers

TitleStatusHype
Generate, Transduct, Adapt: Iterative Transduction with VLMs—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph—0
Cross-Graph Learning of Multi-Relational Associations—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
Hypergraph Pre-training with Graph Neural Networks—0
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning—0
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer—0
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