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 31–40 of 135 papers

TitleStatusHype
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
HONEM: Learning Embedding for Higher Order Networks—0
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Deep Domain Adaptation under Deep Label Scarcity—0
DC Proximal Newton for Non-Convex Optimization Problems—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
An Iterative Co-Training Transductive Framework for Zero Shot Learning—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
Generate, Transduct, Adapt: Iterative Transduction with VLMs—0
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