Transductive Learning
28 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
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Libraries
Use these libraries to find Transductive Learning models and implementationsMost implemented papers
Deep Iterative and Adaptive Learning for Graph Neural Networks
hugochan/IDGL
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In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification
In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples.
Embedding Propagation: Smoother Manifold for Few-Shot Classification
ElementAI/embedding-propagation
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ECCV 2020
Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16\% points.
Predicting Strategic Behavior from Free Text
In ablation analysis, we demonstrate the importance of our modeling choices---the representation of the text with the commonsensical personality attributes and our classifier---to the predictive power of our model.
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
delta2323/GB-GNN
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NeurIPS 2020
By combining it with generalization gap bounds in terms of transductive Rademacher complexity, we show that a test error bound of a specific type of multi-scale GNNs that decreases corresponding to the number of node aggregations under some conditions.
Fast Few-Shot Classification by Few-Iteration Meta-Learning
4rdhendu/fiml
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By employing an efficient initialization module and a Steepest Descent based optimization algorithm, our base learner predicts a powerful classifier within only a few iterations.
BertGCN: Transductive Text Classification by Combining GCN and BERT
ZeroRin/BertGCN
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In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification.
Joint Inductive and Transductive Learning for Video Object Segmentation
In this work, we propose to integrate transductive and inductive learning into a unified framework to exploit the complementarity between them for accurate and robust video object segmentation.
Transductive Learning for Unsupervised Text Style Transfer
The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future.
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
jfc43/eval-transductive-robustness
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ICLR 2022
There has been emerging interest in using transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020; Wang et al., ArXiv 2021).
