Search Results for author: Kaiyang Liao

Found 4 papers, 1 papers with code

High-order structure preserving graph neural network for few-shot learning

1 code implementation29 May 2020 Guangfeng Lin, Ying Yang, Yindi Fan, Xiaobing Kang, Kaiyang Liao, Fan Zhao

Most existing methods try to model the similarity relationship of the samples in the intra tasks, and generalize the model to identify the new categories.

Few-Shot Learning

Deep graph learning for semi-supervised classification

no code implementations29 May 2020 Guangfeng Lin, Xiaobing Kang, Kaiyang Liao, Fan Zhao, Yajun Chen

Existing methods mostly combine the computational layer and the related losses into GCN for exploring the global graph(measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples).

Classification General Classification +1

Structure fusion based on graph convolutional networks for semi-supervised classification

no code implementations2 Jul 2019 Guangfeng Lin, Jing Wang, Kaiyang Liao, Fan Zhao, Wanjun Chen

By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification.

Classification General Classification +1

Transfer feature generating networks with semantic classes structure for zero-shot learning

no code implementations6 Mar 2019 Guangfeng Lin, Wanjun Chen, Kaiyang Liao, Xiaobing Kang, Caixia Fan

To alleviate the negative influence of this inconsistence for ZSL and GZSL, transfer feature generating networks with semantic classes structure (TFGNSCS) is proposed to construct networks model for improving the performance of ZSL and GZSL.

General Classification Zero-Shot Learning

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