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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 51–75 of 135 papers

TitleStatusHype
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
View-Consistent Heterogeneous Network on Graphs With Few Labeled NodesCode1
Sparsity-aware neural user behavior modeling in online interaction platformsCode1
Self-Training: A Survey—0
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot LearningCode0
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Transductive Learning for Abstractive News Summarization—0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image ClassifiersCode0
Inductive Lottery Ticket Learning for Graph Neural Networks—0
Transductive Learning for Unsupervised Text Style TransferCode1
Semi-Supervised Domain Generalizable Person Re-IdentificationCode2
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
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
BertGCN: Transductive Text Classification by Combining GCN and BERTCode1
Transductive Learning for Abstractive News Summarization—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
DINE: Domain Adaptation from Single and Multiple Black-box PredictorsCode1
Learning Graph Neural Networks with Positive and Unlabeled Nodes—0
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