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Fsake: Few-Shot Graph Learning Via Adaptive Neighbor Class Knowledge Embedding

https://doi.org/10.2139/ssrn.4888853
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inferredcites →https://doi.org/10.1007/978-3-030-58574-7_8openalex-snapshot
Embedding Propagation: Smoother Manifold for Few-Shot Classification
inferredcites →https://doi.org/10.1007/978-3-031-20044-1_26openalex-snapshot
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
inferredcites →https://doi.org/10.1016/j.eswa.2023.122173openalex-snapshot
SSL-ProtoNet: Self-supervised Learning Prototypical Networks for few-shot learning
inferredcites →https://doi.org/10.1016/j.eswa.2024.123885openalex-snapshot
HMRM: Hierarchy-aware Misclassification Risk Minimization for few-shot learning
inferredcites →https://doi.org/10.1016/j.ins.2024.120366openalex-snapshot
A preliminary study on few-shot knowledge reasoning mechanism based on three-way partial order structure
inferredcites →https://doi.org/10.1016/j.neucom.2021.09.070openalex-snapshot
Discriminative learning of imaginary data for few-shot classification
inferredcites →https://doi.org/10.1016/j.patcog.2022.108820openalex-snapshot
Multi-granularity episodic contrastive learning for few-shot learning
inferredcites →https://doi.org/10.1016/j.patcog.2022.109170openalex-snapshot
SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning
inferredcites →https://doi.org/10.1016/j.patcog.2023.109480openalex-snapshot
A survey on machine learning from few samples
inferredcites →https://doi.org/10.1016/j.patcog.2023.109702openalex-snapshot
Global- and local-aware feature augmentation with semantic orthogonality for few-shot image classification
inferredcites →https://doi.org/10.1016/j.patcog.2023.109904openalex-snapshot
Few-shot classification guided by generalization error bound
inferredcites →https://doi.org/10.1016/j.patcog.2024.110264openalex-snapshot
Edge-labeling based modified gated graph network for few-shot learning
inferredcites →https://doi.org/10.1109/cvpr.2018.00131openalex-snapshot
Learning to Compare: Relation Network for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00010openalex-snapshot
Edge-Labeling Graph Neural Network for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00743openalex-snapshot
Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01222openalex-snapshot
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01340openalex-snapshot
DPGN: Distribution Propagation Graph Network for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00236openalex-snapshot
Mutual CRF-GNN for Few-shot Learning
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00653openalex-snapshot
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00781openalex-snapshot
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
inferredcites →https://doi.org/10.1109/iccv48922.2021.00870openalex-snapshot
Relational Embedding for Few-Shot Classification
inferredcites →https://doi.org/10.1109/icmew.2019.00041openalex-snapshot
Self-Attention Relation Network for Few-Shot Learning
inferredcites →https://doi.org/10.1109/tcsvt.2021.3058098openalex-snapshot
Hierarchical Graph Neural Networks for Few-Shot Learning
inferredcites →https://doi.org/10.1109/tmm.2022.3233442openalex-snapshot
Graph Neural Networks With Triple Attention for Few-Shot Learning
inferredcites →https://doi.org/10.1109/tnnls.2021.3082928openalex-snapshot
Transductive Relation-Propagation With Decoupling Training for Few-Shot Learning
inferredcites →https://doi.org/10.1109/tnnls.2022.3172879openalex-snapshot
GUIDE: Training Deep Graph Neural Networks via Guided Dropout Over Edges
inferredcites →https://doi.org/10.1109/tnnls.2022.3178849openalex-snapshot
Graph Few-Shot Learning via Restructuring Task Graph
inferredcites →https://doi.org/10.1109/tnnls.2024.3380833openalex-snapshot
CSTS: Exploring Class-Specific and Task-Shared Embedding Representation for Few-Shot Learning
inferredcites →https://doi.org/10.1109/tpami.2021.3107164openalex-snapshot
Power Normalizations in Fine-Grained Image, Few-Shot Image and Graph Classification
inferredcites →https://doi.org/10.1109/tpami.2023.3312125openalex-snapshot
LibFewShot: A Comprehensive Library for Few-Shot Learning
inferredcites →https://doi.org/10.1109/wacv56688.2023.00250openalex-snapshot
HyperShot: Few-Shot Learning by Kernel HyperNetworks
inferredcites →https://doi.org/10.1609/aaai.v36i3.20226openalex-snapshot
Hybrid Graph Neural Networks for Few-Shot Learning
inferredcites →https://doi.org/10.2139/ssrn.4611614openalex-snapshot
Few-Shot Object Detection: Research Advances and Challenges
inferredcites →https://doi.org/10.24963/ijcai.2020/112openalex-snapshot
Transductive Relation-Propagation Network for Few-shot Learning
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1703.03400openalex-snapshot
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1703.05175openalex-snapshot
Prototypical Networks for Few-shot Learning
inferredcites →https://doi.org/10.48550/arxiv.1803.00676openalex-snapshot
Meta-Learning for Semi-Supervised Few-Shot Classification
inferredcites →https://doi.org/10.48550/arxiv.1805.08136openalex-snapshot
Meta-learning with differentiable closed-form solvers
inferredcites →https://doi.org/10.48550/arxiv.1805.10002openalex-snapshot
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
inferredcites →https://doi.org/10.48550/arxiv.1909.09157openalex-snapshot
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot
Caltech-UCSD Birds-200-2011 Dataset
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