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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 61–70 of 135 papers

TitleStatusHype
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling—0
Understanding Generalization via Leave-One-Out Conditional Mutual Information—0
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
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
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
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