"Undistillable: Making A Nasty Teacher That CANNOT teach students"

Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang
In ICLR 2021 Spotlight Oral


  • We propose the concept of Nasty Teacher, a defensive approach to prevent knowledge leaking and unauthorized model cloning through KD without sacrificing performance.
  • We propose a simple yet efficient algorithm, called self-undermining knowledge distillation, to directly build a nasty teacher through self-training, requiring no additional dataset
    nor auxiliary network.


We use Pytorch 1.4.0, and CUDA 10.1. You can install them with

conda install pytorch=1.4.0 torchvision=0.5.0 cudatoolkit=10.1 -c pytorch

It should also be applicable to other Pytorch and CUDA versions.

Then install other packages by

pip install -r requirements.txt


Teacher networks

Step 1: Train a normal teacher network
python train_scratch.py --save_path [XXX]

Here, [XXX] specifies the directory of params.json, which contains all hyperparameters to train a network.
We already include all hyperparameters in experiments to reproduce the results in our paper.

For example, normally train a ResNet18 on CIFAR-10

python train_scratch.py --save_path experiments/CIFAR10/baseline/resnet18

After finishing training, you will get training.log, best_model.tar in that directory.

The normal teacher network will serve as the adversarial network for the training of the nasty teacher.

Step 2: Train a nasty teacher network
python train_nasty.py --save_path [XXX]

Again, [XXX] specifies the directory of params.json,
which contains the information of adversarial networks and hyperparameters for training.
You need to specify the architecture of adversarial network and its checkpoint in this file.

For example, train a nasty ResNet18

python train_nasty.py --save_path experiments/CIFAR10/kd_nasty_resnet18/nasty_resnet18

Knowledge Distillation for Student networks

You can train a student distilling from normal or nasty teachers by

python train_kd.py --save_path [XXX]

Again, [XXX] specifies the directory of params.json,
which contains the information of student networks and teacher networks

For example,

  • train a plain CNN distilling from a nasty ResNet18
python train_kd.py --save_path experiments/CIFAR10/kd_nasty_resnet18/cnn
  • Train a plain CNN distilling from a normal ResNet18
python train_kd.py --save_path experiments/CIFAR10/kd_normal_resnet18/cnn


title={Undistillable: Making A Nasty Teacher That {\{}CANNOT{\}} teach students},
author={Haoyu Ma and Tianlong Chen and Ting-Kuei Hu and Chenyu You and Xiaohui Xie and Zhangyang Wang},
booktitle={International Conference on Learning Representations},