CorrReg

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Code for paper "Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers"

CorrReg 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers
Created by Kui Jia, Jiehong Lin, Mingkui Tan and Dacheng Tao.

### Introduction
This is the code released for our work "Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers" published in TIP2019. In this work, we study the problem of learning deep representations from multi-view data in end-to-end networks. We take a regularization approach via multi-view learning criteria, and propose a novel, effective, and efficient neuron-wise correlation-maximizing regularizer. We implement our proposed regularizers collectively as a correlation-regularized network layer (CorrReg).

### Implementations
* [Torch](https://github.com/MortimerLin/CorrReg/tree/master/RGBD-ConvNet-torch)
* [Pytorch](https://github.com/MortimerLin/CorrReg/tree/master/MvFusionNet-pytorch)

### Citation
If you find our work useful in your research, please consider citing:

```
@article{jia2019deep,
      title={Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers},
      author={Jia, Kui and Lin, Jiehong and Tan, Mingkui and Tao, Dacheng},
      journal={IEEE Transactions on Image Processing},
      year={2019},
      publisher={IEEE}
    }
```

### Contact
`kuijia@scut.edu.cn`

`lin.jiehong@mail.scut.edu.cn`

CorrReg

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