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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 126–135 of 135 papers

TitleStatusHype
Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial ActivationsCode0
Distributed representations of graphs for drug pair scoringCode0
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language ModelsCode0
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
Fast Online Node Labeling for Very Large GraphsCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image ClassifiersCode0
Few-shot Novel Category DiscoveryCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot LearningCode0
TransBoost: Improving the Best ImageNet Performance using Deep TransductionCode0
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