Zero-Shot Learning with Common Sense Knowledge Graphs

Autor:	Nayak, Nihal V., Bach, Stephen H.
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Paper published in TMLR
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.10713 Zobrazit plný text záznamu View this record from Arxiv