Graph-based-Few-shot-Learning-Papers

I will continuously update the Few-shot Learning papers based on the Graph Neural Networks.

2021

1. AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning, WACV, 2021
2. Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph, AAAI, 2021
3. Mutual CRF-GNN for Few-shot Learning , CVPR, 2021
4. PTN: A Poisson transfer network for semi-supervised few-shot learning, AAAI, 2021

2020

1. DPGN: Distribution Propagation Graph Network for Few-shot Learning, CVPR, 2020

2. Graph few-shot learning via knowledge transfer, AAAI, 2020

3. Graph prototypical networks for few-shot learning on attributed networks, CIKM, 2020

4. Towards locality-aware meta-learning of tail node embeddings on networks, CIKM, 2020

5. Model-Agnostic Graph Regularization for Few-Shot Learning, NeurIPS Workshop on Meta-Learning, 2020

6. Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification, CIKM, 2020

7. Graph Few-shot Learning with Attribute Matching, CIKM, 2020

2019

1. Few-Shot Audio Classification with Attentional Graph Neural Networks, INTERSPEECH, 2019

2. Edge-Labeling_Graph_Neural_Network_for_Few-Shot_Learning, CVPR, 2019

3. Learning to propagate labels: Transductive propagation network for few-shot learning, ICLR, 2019

2018

2017

1. Few-shot learning with graph neural networks, arXiv, 2017