MosaicKD

Code for NeurIPS-21 paper "Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data"

1. Motivation

Natural images share common local patterns. In MosaicKD, these local patterns are first dissembled from OOD data and then assembled to synthesize in-domain data, making OOD-KD feasible.

2. Method

MosaicKD establishes a four-player minimax game between a generator G, a patch discriminator D, a teacher model T and a student model S. The generator, as those in prior GANs, takes as input a random noise vector and learns to mosaic synthetic in-domain samples with locally-authentic and globally-legitimate distributions, under the supervisions back-propagated from the other three players.

3. Reproducing our results

3.1 Prepare teachers

Please download our pre-trained models from Dropbox (266 M) and extract them as "checkpoints/pretrained/*.pth". You can also train your own models as follows:

python train_scratch.py --lr 0.1 --batch-size 256 --model wrn40_2 --dataset cifar100

3.2 OOD-KD: CIFAR-100 (ID) + CIFAR10 (OOD)

• Vanilla KD (Blind KD)

  python kd_vanilla.py --lr 0.1 --batch-size 128 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --gpu 0 

• Data-Free KD (DFQAD)

  python kd_datafree.py --lr 0.1 --batch-size 256 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --lr 0.1 --local 1 --align 1 --adv 1 --balance 10 --gpu 0

• MosaicKD (This work)

  python kd_mosaic.py --lr 0.1 --batch-size 256 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --lr 0.1 --local 1 --align 1 --adv 1 --balance 10 --gpu 0

3.3 OOD-KD: CIFAR-100 (ID) + ImageNet/Places365 OOD Subset (OOD)

• Prepare 32x32 datasets
  Please prepare the 32x32 ImageNet following the instructions from https://patrykchrabaszcz.github.io/Imagenet32/ and extract them as "data/ImageNet_32x32/train" and "data/ImageNet_32x32/val". You can prepare Places365 in the same way.

• MosaicKD on OOD subset
  As ImageNet & Places365 contain a large number of in-domain samples, we construct OOD subset for training. Please run the scripts with ''--ood_subset'' to enable subset selection.

  python kd_mosaic.py --lr 0.1 --batch-size 256 --teacher wrn40_2 --student wrn16_1 --dataset cifar100 --unlabeled cifar10 --epoch 200 --lr 0.1 --local 1 --align 1 --adv 1 --balance 10 --ood_subset --gpu 0

4. Visualization of synthetic data

5. Citation

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

@article{fang2021mosaicking,
  title={Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data},
  author={Gongfan Fang and Yifan Bao and Jie Song and Xinchao Wang and Donglin Xie and Chengchao Shen and Mingli Song},
  journal={arXiv preprint arXiv:2110.15094},
  year={2021}
}