SOTAVerified

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 71–80 of 135 papers

TitleStatusHype
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image ClassifiersCode0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Transductive image segmentation: Self-training and effect of uncertainty estimation—0
Towards Adversarial Robustness via Transductive Learning—0
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph—0
Hypergraph Pre-training with Graph Neural Networks—0
Transductive Learning for Abstractive News Summarization—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
Learning Graph Neural Networks with Positive and Unlabeled Nodes—0
Fast Few-Shot Classification by Few-Iteration Meta-LearningCode0
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