MIX-KD

This repo is the official Pytorch code for our WACV 2023, "Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study" paper

Requirement

This code was tested with Python 3.8.12, Torch 1.7.1, and CUDA 11.0. But it should work with a recent version of the torch.

Running

Please run the train_student_MIXSTD.py code to reproduce the results in the main paper. Also, note that you might want to adjust some hyper-parameters as follows:

1. Download pretrained teacher model via RepDistiller.

2. Hyper-parameters

   • pmixup : If True, use the PMU (partial mixup). Otherwise, the FMU (full mixup) will be applied.
   • partmixup : the amount of mixup pairs as described in the paper.
   • gamma : weighting on the cross-entropy loss.
   • alpha : weighting on the KD loss.
   • beta_a : parameter in the beta distribution for the mixup.

ToDo

• Adversarial attack on the CIFAR100
• Visualization code using tSNE
• Training code on the CIFAR100

Acknowledgement

I thank Yonglong for providing a very well-organized code. We tested our model based on their code.