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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/4rdhendu/fiml

공식 코드

Fast Few-Shot Classification by Few-Iteration Meta-Learning

https://github.com/ElementAI/embedding-propagation

공식 코드

Embedding Propagation: Smoother Manifold for Few-Shot Classification

https://github.com/JDAI-CV/fast-reid

공식 코드

Semi-Supervised Domain Generalizable Person Re-Identification

https://github.com/LorenzoBini4/FlowCyt-Classification-Benchmark

공식 코드

FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking

https://github.com/MIRALab-USTC/TOP

공식 코드

Accurate and Scalable Graph Neural Networks via Message Invariance

https://github.com/MaxZanella/transduction-for-vlms

공식 코드

Boosting Vision-Language Models with Transduction

https://github.com/ZeroRin/BertGCN

공식 코드

BertGCN: Transductive Text Classification by Combining GCN and BERT

https://github.com/ahmetius/LP-DeepSSL

공식 코드

Label Propagation for Deep Semi-supervised Learning

https://github.com/aravindsankar28/Inf-VAE

공식 코드

Sparsity-aware neural user behavior modeling in online interaction platforms

https://github.com/ashengl/fsncd

공식 코드

Few-shot Novel Category Discovery

https://github.com/baojian/FastONL

공식 코드

Fast Online Node Labeling for Very Large Graphs

https://github.com/c4dm/dcase-few-shot-bioacoustic

공식 코드

Few-shot bioacoustic event detection at the DCASE 2022 challenge

https://github.com/chapagaisa/transductive

공식 코드

Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse

https://github.com/delta2323/GB-GNN

공식 코드

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks

https://github.com/git-ljc/umfc

공식 코드

UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models

https://github.com/graphdml-uiuc-jlu/geom-gcn

공식 코드

Geom-GCN: Geometric Graph Convolutional Networks

https://github.com/hugochan/IDGL

공식 코드

Deep Iterative and Adaptive Learning for Graph Neural Networks

https://github.com/jfc43/eval-transductive-robustness

공식 코드

Towards Evaluating the Robustness of Neural Networks Learned by Transduction

https://github.com/leduckhai/wav2graph

공식 코드

wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech

https://github.com/leonresearch/city-networks

공식 코드

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

https://github.com/lokhande-vishnu/DeepHermites

공식 코드

Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations

https://github.com/maoyunyao/joint

공식 코드

Joint Inductive and Transductive Learning for Video Object Segmentation

https://github.com/omerb01/transboost

공식 코드

TransBoost: Improving the Best ImageNet Performance using Deep Transduction

https://github.com/omerbp/Predicting-NLPGT

공식 코드

Predicting Strategic Behavior from Free Text

https://github.com/paulmorio/drugpairscoringdr

공식 코드

Distributed representations of graphs for drug pair scoring

https://github.com/plai-group/simple-cnaps

공식 코드

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

https://github.com/sunisfighting/signa

공식 코드

Single-View Graph Contrastive Learning with Soft Neighborhood Awareness

https://github.com/tim-learn/Dis-tune

공식 코드

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

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

공식 코드

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

https://github.com/ulab-uiuc/graphrouter

공식 코드

GraphRouter: A Graph-based Router for LLM Selections

https://github.com/vladan-stojnic/zlap

공식 코드

Label Propagation for Zero-shot Classification with Vision-Language Models

https://github.com/xiaofei05/tsst

공식 코드

Transductive Learning for Unsupervised Text Style Transfer

https://github.com/yhu01/transfer-sgc

공식 코드

Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification

https://github.com/AntreasAntoniou/Learning_to_Learn_via_Self-Critique

Learning to learn via Self-Critique

https://github.com/KAIDI3270/Geom_GCN_pytorch_implementation

Geom-GCN: Geometric Graph Convolutional Networks

https://github.com/L1aoXingyu/reid_baseline

Semi-Supervised Domain Generalizable Person Re-Identification

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