CIARD

July 14, 2025 · View on GitHub

WE HAVE BEEN ACCEPTED AT ICCV2025!cheeeeeeeeeeers! soon we will upload more detailed and polished codes and more ckpts!

CIARD: Enhancing Accuracy and Robustness of Student Models through Cyclic Iterative Distillation

Instructions for Reproducing Results

  1. Environment Setup
    Ensure you are using Python 3.8. Install all required packages using:

    pip install -r requirements.txt
    
  2. Download Teacher Models

    • Download the clean teacher model checkpoint and place it in:
      models/nat_teacher_checkpoint/
    • Download the robust teacher model and place it accordingly.
      The models we used can be found here.
  3. Dataset

    • Store the dataset in the data/ folder.
  4. Run the Model

    • To run CIARD, use:
    python CIARD.py
    
    • You can modify the configuration in CIARD.py to change the student architecture or dataset.

    • To run evaluation, use:

    python attack_eval.py
    
    • You can(should) modify the configuration in attack_eval.py to set the student path.