Zero-Shot Learning with Common Sense Knowledge Graphs
- URL: http://arxiv.org/abs/2006.10713v4
- Date: Thu, 25 Aug 2022 19:27:00 GMT
- Title: Zero-Shot Learning with Common Sense Knowledge Graphs
- Authors: Nihal V. Nayak, Stephen H. Bach
- Abstract summary: We propose to learn class representations by embedding nodes from common sense knowledge graphs in a vector space.
We introduce ZSL-KG, a general-purpose framework with a novel transformer graph convolutional network (TrGCN) for generating class representations.
Our results show that ZSL-KG improves over existing WordNet-based methods on five out of six zero-shot benchmark datasets in language and vision.
- Score: 10.721717005752405
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Zero-shot learning relies on semantic class representations such as
hand-engineered attributes or learned embeddings to predict classes without any
labeled examples. We propose to learn class representations by embedding nodes
from common sense knowledge graphs in a vector space. Common sense knowledge
graphs are an untapped source of explicit high-level knowledge that requires
little human effort to apply to a range of tasks. To capture the knowledge in
the graph, we introduce ZSL-KG, a general-purpose framework with a novel
transformer graph convolutional network (TrGCN) for generating class
representations. Our proposed TrGCN architecture computes non-linear
combinations of node neighbourhoods. Our results show that ZSL-KG improves over
existing WordNet-based methods on five out of six zero-shot benchmark datasets
in language and vision.
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