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

Showing 110 of 135 papers

TitleStatusHype
Semi-Supervised Domain Generalizable Person Re-IdentificationCode2
Boosting Vision-Language Models with TransductionCode2
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event DetectionCode2
wav2graph: A Framework for Supervised Learning Knowledge Graph from SpeechCode2
Transductive Active Learning: Theory and ApplicationsCode2
GraphRouter: A Graph-based Router for LLM SelectionsCode2
Embedding Propagation: Smoother Manifold for Few-Shot ClassificationCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
DINE: Domain Adaptation from Single and Multiple Black-box PredictorsCode1
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