DataFree

A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

Authors: Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, Mingli Song

CMI (this work)	DeepInv
ZSKT	DFQ

Results

1. CIFAR-10

Method	resnet-34 resnet-18	vgg-11 resnet-18	wrn-40-2 wrn-16-1	wrn-40-2 wrn-40-1	wrn-40-2 wrn-16-2
T. Scratch	95.70	92.25	94.87	94.87	94.87
S. Scratch	95.20	95.20	91.12	93.94	93.95
DAFL	92.22	81.10	65.71	81.33	81.55
ZSKT	93.32	89.46	83.74	86.07	89.66
DeepInv	93.26	90.36	83.04	86.85	89.72
DFQ	94.61	90.84	86.14	91.69	92.01
CMI	94.84	91.13	90.01	92.78	92.52

2. CIFAR-100

Method	resnet-34 resnet-18	vgg-11 resnet-18	wrn-40-2 wrn-16-1	wrn-40-2 wrn-40-1	wrn-40-2 wrn-16-2
T. Scratch	78.05	71.32	75.83	75.83	75.83
S. Scratch	77.10	77.01	65.31	72.19	73.56
DAFL	74.47	57.29	22.50	34.66	40.00
ZSKT	67.74	34.72	30.15	29.73	28.44
DeepInv	61.32	54.13	53.77	61.33	61.34
DFQ	77.01	68.32	54.77	62.92	59.01
CMI	77.04	70.56	57.91	68.88	68.75

Quick Start

1. Visualize the inverted samples

Results will be saved as checkpoints/datafree-cmi/synthetic-cmi_for_vis.png

bash scripts/cmi/cmi_cifar10_for_vis.sh

2. Reproduce our results

Note: This repo was refactored from our experimental code and is still under development. I'm struggling to find the appropriate hyperparams for every methods (°ー°〃). So far, we only provide the hyperparameters to reproduce CIFAR-10 results for wrn-40-2 => wrn-16-1. You may need to tune the hyper-parameters for other models and datasets. More resources will be uploaded in the future update.

To reproduce our results, please download pre-trained teacher models from Dropbox-Models (266 MB) and extract them as checkpoints/pretrained. Also a pre-inverted data set with ~50k samples is available for wrn-40-2 teacher on CIFAR-10. You can download it from Dropbox-Data-Preinverted (133 MB) and extract them to run/cmi-preinverted-wrn402/.

• Non-adversarial CMI: you can train a student model on inverted data directly. It should reach the accuracy of ~87.38% on CIFAR-10 as reported in Figure 3.

  bash scripts/cmi/nonadv_cmi_cifar10_wrn402_wrn161.sh
  

• Adversarial CMI: or you can apply the adversarial distillation based on the pre-inverted data from the above-mentioned non-adversarial process. About 10k (256x40) new samples will be generated to improve the student, which should achieve the accuracy of ~90.01% on CIFAR-10 as reported in Table 1.

  bash scripts/cmi/adv_cmi_cifar10_wrn402_wrn161.sh
  

• Scratch CMI: It is OK to run the cmi algorithm wihout any pre-inverted data, but the student may overfit to some early data. In this case, the student should achieve the accuracy of ~88.82% on CIFAR-10, slightly worse than adversarial CMI (90.01%).

  bash scripts/cmi/scratch_cmi_cifar10_wrn402_wrn161.sh
  

3. Scratch training

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

4. Vanilla KD

# KD with original training data (beta>0 to use hard targets)
python vanilla_kd.py --teacher wrn40_2 --student wrn16_1 --dataset cifar10 --transfer_set cifar10 --beta 0.1 --batch-size 128 --lr 0.1 --epoch 200 --gpu 0 

# KD with unlabeled data
python vanilla_kd.py --teacher wrn40_2 --student wrn16_1 --dataset cifar10 --transfer_set cifar100 --beta 0 --batch-size 128 --lr 0.1 --epoch 200 --gpu 0 

# KD with unlabeled data from a specified folder
python vanilla_kd.py --teacher wrn40_2 --student wrn16_1 --dataset cifar10 --transfer_set run/cmi --beta 0 --batch-size 128 --lr 0.1 --epoch 200 --gpu 0 

5. Data-free KD

bash scripts/xxx/xxx.sh # e.g. scripts/zskt/zskt_cifar10_wrn402_wrn161.sh

Hyper-parameters used by different methods:

Method	adv	bn	oh	balance	act	cr	GAN	Example
DAFL	-	-	√	√	√	-	√	scripts/dafl_cifar10.sh
ZSKT	√	-	-	-	-	-	√	scripts/zskt_cifar10.sh
DeepInv	√	√	√	-	-	-	-	scripts/deepinv_cifar10.sh
DFQ	√	√	√	√	-	-	√	scripts/dfq_cifar10.sh
CMI	√	√	√	-	-	√	√	scripts/cmi_cifar10_scratch.sh

4. Use your models/datasets

You can register your models and datasets in registry.py by modifying NORMALIZE_DICT, MODEL_DICT and get_dataset. Then you can run the above commands to train your own models. As DAFL requires intermediate features from the penultimate layer, your model should accept an return_features=True parameter and return a (logits, features) tuple for DAFL.

5. Implement your algorithms

Your algorithms should inherent datafree.synthesis.BaseSynthesizer to implement two interfaces: 1) BaseSynthesizer.synthesize takes several steps to craft new samples and return an image dict for visualization; 2) BaseSynthesizer.sample fetches a batch of training data for KD.

Citation

If you found this work useful for your research, please cite our paper:

@article{fang2021contrastive,
  title={Contrastive Model Inversion for Data-Free Knowledge Distillation},
  author={Fang, Gongfan and Song, Jie and Wang, Xinchao and Shen, Chengchao and Wang, Xingen and Song, Mingli},
  journal={arXiv preprint arXiv:2105.08584},
  year={2021}
}

Reference

• ZSKT: Zero-shot Knowledge Transfer via Adversarial Belief Matching
• DAFL: Data-Free Learning of Student Networks
• DeepInv: Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
• DFQ: Data-Free Network Quantization With Adversarial Knowledge Distillation