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Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph convolutional networks (GCN) that handle non-grid data.
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Semi-supervised classification with graph convolutional networks
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Accelerating convolutional neural networks with dominant convolutional kernel and knowledge pre-regression
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
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Data-free learning of student networks
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
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On compressing deep models by low rank and sparse decomposition
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Hypergraph neural networks
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Knowledge flow: Improve upon your teachers
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Knowledge distillation via instance relationship graph
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Knowledge distillation via instance relationship graph
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Knowledge amalgamation from heterogeneous networks by common feature learning
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Amalgamating knowledge towards comprehensive classification
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Customizing student networks from heterogeneous teachers via adaptive knowledge amalgamation
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Binarized collaborative filtering with distilling graph convolutional networks
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Amalgamating filtered knowledge: Learning task-customized student from multi-task teachers
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