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Found 5 papers, 0 papers with code
GA-HQS: MRI reconstruction via a generically accelerated unfolding approach
Deep unfolding networks (DUNs) are the foremost methods in the realm of compressed sensing MRI, as they can employ learnable networks to facilitate interpretable forward-inference operators.
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.
Ranked #33 on
Node Classification
on Citeseer
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition
In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.
Ranked #54 on
Skeleton Based Action Recognition
on NTU RGB+D
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.
Class label autoencoder for zero-shot learning
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.
