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Found 4 papers, 1 papers with code
High-order structure preserving graph neural network for few-shot learning
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.
Deep graph learning for semi-supervised classification
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).
Structure fusion based on graph convolutional networks for semi-supervised classification
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.
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Transfer feature generating networks with semantic classes structure for zero-shot learning
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.
