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Zero-Shot Knowledge Distillation in Deep Networks Pytorch

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PyTorch implementation for Zero-Shot Knowledge Distillation in Deep Networks

Install

You will need a machine with a GPU and CUDA installed.
Then, you prepare runtime environment:

pip install -r requirements.txt

Use

For mnist dataset,

python main.py --dataset=mnist --t_train=False --num_sample=12000 --batch_size=200 

For cifar10 dataset,

python main.py --dataset=cifar10 --t_train=False --num_sample=24000 --batch_size=100

Arguments:

• dataset - available dataset: ['mnist', 'cifar10', 'cifar100']
• t_train - Train teacher network??
  • if True, train teacher network
  • elif False, load trained teacher network
• num_sample - Number of DIs crafted per category
• beta - Beta scaling vectors
• batch_size - batch size
• lr - learning rate
• iters - iteration number
• s_save_path - save path for student network
• do_genimgs - generate synthesized images from ZSKD??
  • if True, generate images
  • elif False, you must have the synthesized images that are generated from ZSKD

Result examples for MNIST dataset

Understanding this method(algorithm)

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