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Knowledge Distillation by On the fly Native Ensemble （ONE）NeurIPS2018

This is an Pytorch implementation of Xu et al. Knowledge Distillation On the Fly Native Ensemble (ONE) NeurIPS 2018 on Python 2.7, Pytorch 2.0. You may refer to our Vedio and Poster for a quick overview.

ONE

Getting Started

Prerequisites:

  • Datasets: CIFAR100, CIFAR10
  • Python 2.7.
  • Pytorch version == 0.2.0.

Running Experiments

you may need change GPU-ID in scripts， “--gpu-id”， the default is 0.

Training:

For example, to train the ONE model using ResNet-32 or ResNet-110 on CIFAR100, run the the following scripts.

bash scripts/ONE_ResNet32.sh
bash scripts/ONE_ResNet110.sh

To train baseline model using ResNet-32 or ResNet-110 on CIFAR100, run the the following scripts.

bash scripts/Baseline_ResNet32.sh
bash scripts/Baseline_ResNet110.sh

Tip for Stabilizing Model Training

It may help to ramp up [https://arxiv.org/abs/1703.01780] the KL cost in the beginning over the first few epochs until the teacher network starts giving good predictions.

Citation

Please refer to the following if this repository is useful for your research.

Bibtex:

@inproceedings{lan2018knowledge,
  title={Knowledge Distillation by On-the-Fly Native Ensemble},
  author={Lan, Xu and Zhu, Xiatian and Gong, Shaogang},
  booktitle={Advances in Neural Information Processing Systems},
  pages={7527--7537},
  year={2018}
}

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgements

This repository is partially built upon the bearpaw/pytorch-classification repository.