Zero-Shot Learning with Common Sense Knowledge Graphs

Code

• github.com/BatsResearch/zsl-kg
  OfficialIn paperpytorch★ 113
• github.com/BatsResearch/nayak-arxiv20-code
  OfficialIn paperpytorch★ 9
• github.com/batsresearch/nayak-tmlr22-code
  OfficialIn paperpytorch★ 9

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.

Tasks

Benchmark Results

Reproductions