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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 51–60 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 Learning—0
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Transductive Learning for Abstractive News Summarization—0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
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