Search Results for author: Fan Zhao

Found 6 papers, 2 papers with code

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).

General Classification Graph Learning

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

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.

General Classification Node Classification

Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network

no code implementations CVPR 2018 Wenda Zhao, Fan Zhao, Dong Wang, Huchuan Lu

To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD.

Defocus Estimation

Class label autoencoder for zero-shot learning

no code implementations25 Jan 2018 Guangfeng Lin, Caixia Fan, Wanjun Chen, Yajun Chen, Fan Zhao

CLA can not only build a uniform framework for adapting to multi-semantic embedding spaces, but also construct the encoder-decoder mechanism for constraining the bidirectional projection between the feature space and the class label space.

Zero-Shot Learning

Structure propagation for zero-shot learning

1 code implementation27 Nov 2017 Guangfeng Lin, Yajun Chen, Fan Zhao

It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL.

Zero-Shot Learning

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