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Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

https://arxiv.org/abs/2108.05507

model

cifia-100 result

result

The implementation of compared methods are based on the author-provided code and a open-source benchmark https://github.com/HobbitLong/RepDistiller.

Installation

conda install --yes --file requirements.txt

Running

  1. Fetch the pretrained teacher models by:

    sh scripts/fetch_pretrained_teachers.sh

    which will download and save the models to save/models

  2. Run distillation by commands in scripts\run_cifar_distill.sh. An example of running HKD is given by:

    python train_student.py --path_t ./save/models/resnet32x4_vanilla/ckpt_epoch_240.pth --distill hkd --model_s resnet8x4 -a 1 -b 3 --mode hkd --trial 1

Citation

@InProceedings{Zhou_2021_ICCV,
    author    = {Zhou, Sheng and Wang, Yucheng and Chen, Defang and Chen, Jiawei and Wang, Xin and Wang, Can and Bu, Jiajun},
    title     = {Distilling Holistic Knowledge With Graph Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {10387-10396}
}