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Transductive Learning

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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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/alexfanjn/geomgcn_pyg

Geom-GCN: Geometric Graph Convolutional Networks

https://github.com/bingzhewei/geom-gcn

Geom-GCN: Geometric Graph Convolutional Networks

https://github.com/jonhue/activeft

Transductive Active Learning: Theory and Applications

https://github.com/jonhue/transductive-active-learning

Transductive Active Learning: Theory and Applications

https://github.com/peymanbateni/simple-cnaps

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

https://github.com/tim-learn/SHOT

DINE: Domain Adaptation from Single and Multiple Black-box Predictors

https://github.com/tiosisai/bioac-active-learning-eusipco-2025

Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event Detection

https://github.com/xiaomingzhid/sskd

Semi-Supervised Domain Generalizable Person Re-Identification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.