Rethinking knowledge graph propagation for zero-shot learning

M Kampffmeyer, Y Chen, X Liang… - Proceedings of the …, 2019 - openaccess.thecvf.com
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …
被引用次数：380 相关文章

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang… - openreview.net
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …
[PDF] mit.edu

[PDF][PDF] Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang… - people.csail.mit.edu
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …

Rethinking knowledge graph propagation for zero-shot learning

M Kampffmeyer, Y Chen, X Liang… - 32nd IEEE/CVF …, 2019 - researchwithrutgers.com
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …
[PDF] 45.33.70.161

[PDF][PDF] Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang… - 45.33.70.161
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang… - arXiv preprint arXiv …, 2018 - arxiv.org
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …
[PDF] researchgate.net

[PDF][PDF] Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang… - arXiv preprint arXiv …, 2018 - researchgate.net
The potential of graph convolutional neural networks for the task of zero-shot learning has
been demonstrated recently. These models are highly sample efficient as related concepts …

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang… - arXiv e …, 2018 - ui.adsabs.harvard.edu
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …

Rethinking knowledge graph propagation for zero-shot learning

MC Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang… - 2019 - munin.uit.no
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …
[PDF] cmu.edu

[PDF][PDF] Rethinking Knowledge Graph Propagation for Zero-Shot Learning

M Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang… - cs.cmu.edu
Graph convolutional neural networks have recently shown great potential for the task of zero-
shot learning. These models are highly sample efficient as related concepts in the graph …