most citedHigh-order structure preserving graph neural network for few-shot learning

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

cs.CV2020★ 2 cited

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

Guangfeng Lin, Ying Yang, Yindi Fan +3

Few-shot learning can find the latent structure information between the prior knowledge and the queried data by the similarity metric of meta-learning to construct the discriminati…

cs.CV2020

Deep graph learning for semi-supervised classification

Guangfeng Lin, Xiaobing Kang, Kaiyang Liao +2

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph…

cs.CV2019

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

Guangfeng Lin, Wanjun Chen, Kaiyang Liao +2

Feature generating networks face to the most important question, which is the fitting difference (inconsistence) of the distribution between the generated feature and the real data…