To reproduce our results, please download pre-trained teacher models from Dropbox-Models (266 MB) and extract them as checkpoints/pretrained. Instead, you can train a model from scratch as follows.

python train_scratch.py --model wrn40_2 --dataset cifar10 --batch-size 256 --lr 0.1 --epoch 200 --gpu 0

• To get similar results of our method on CIFAR datasets, run the script in scripts/'. (A sample is shown below) Synthesized images and logs will be saved in checkpoints/nayer`.

  # g-steps is the number of iterations in synthesizing
  python datafree_kd.py --batch_size 512 --synthesis_batch_size 400 --lr 0.2 --gpu 0 --warmup 20 --epochs 120 \
  --dataset cifar100 --method nayer --lr_g 4e-3 --teacher wrn40_2 --student wrn16_2 --save_dir run/c100w402w162-nayer \
  --adv 1.33 --bn 10.0 --oh 0.5 --g_steps 40 --g_life 10 --g_loops 2 --gwp_loops 10 \
  --log_tag c100w402w162-nayer-ep120

@inproceedings{nayer,
title={Nayer: Noisy layer data generation for efficient and effective data-free knowledge distillation},
author={Tran, Minh-Tuan and Le, Trung and Le, Xuan-May and Harandi, Mehrtash and Tran, Quan Hung and Phung, Dinh},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={23860--23869},
year={2024}
}