Rethinking Knowledge Graph Propagation for Zero-Shot Learning

CVPR 2019 Michael KampffmeyerYinbo ChenXiaodan LiangHao WangYujia ZhangEric P. Xing

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 structure share statistical strength allowing generalization to new classes when faced with a lack of data... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.