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This is the project page of our paper:

"Information Competing Process for Learning Diversified Representations." Hu, J., Ji, R., Zhang, S., Sun, X., Ye, Q., Lin, C. W., & Tian, Q. In NeurIPS 2019. [Paper] [Poster] [中文简介]

If you have any problem, please feel free to contact us. (hujie.cpp@gmail.com)

1. Supervised Setting: Classification Task

The codes, usages, models and results for classification task can be found in: ./Classification/

We implement ICP to train VGG16, GoogLeNet, ResNet20 and DenseNet40 on Cifar10 and Cifar100 datasets.

Our codes for the classification task are based on pytorch-cifar and the models from KSE.

2. Self-Supervised Setting: Disentanglement Task

The codes, usages, models and results for disentanglement task can be found in: ./Disentanglement/

We implement ICP to train Beta-VAE on dSprites, 3D Faces and CelebA datasets.

Our codes for the disentanglement task are based on Beta-VAE.

The evaluation metric (MIG) for disentanglement are from beta-tcvae, and we thank Ricky for helping us to use the 3D Faces dataset.

3. Citation

If our paper helps your research, please cite it in your publications:

@inproceedings{hu2019information,
  title={Information Competing Process for Learning Diversified Representations},
  author={Hu, Jie and Ji, Rongrong and Zhang, ShengChuan and Sun, Xiaoshuai and Ye, Qixiang and Lin, Chia-Wen and Tian, Qi},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}

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The project page of paper: Information Competing Process for Learning Diversified Representations [NeurIPS 2019]

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