ml-research / CAKE

Code for our paper Deep Classifier Mimicry without Data Access

Deep Classifier Mimicry without Data Access

Code for our paper Deep Classifier Mimicry without Data Access; Steven Braun, Martin Mundt, and Kristian Kersting; International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

Abstract: Access to pre-trained models has recently emerged as a standard across numerous machine learning domains. Unfortunately, access to the original data the models were trained on may not equally be granted. This makes it tremendously challenging to fine-tune, compress models, adapt continually, or to do any other type of data-driven update. We posit that original data access may however not be required. Specifically, we propose Contrastive Abductive Knowledge Extraction (CAKE), a model-agnostic knowledge distillation procedure that mimics deep classifiers without access to the original data. To this end, CAKE generates pairs of noisy synthetic samples and diffuses them contrastively toward a model's decision boundary. We empirically corroborate CAKE's effectiveness using several benchmark datasets and various architectural choices, paving the way for broad application.

Examples

Run CAKE on MNIST:

python src/main.py experiment=mnist-cnn

We use hydra's multirun feature enabled with the -m/--multirun flag and can specify multiple values for specific configurations (e.g. sampling.noise as below).

python src/main.py -m sampling.noise=1e-3,1e-2,1e-1

Configurations are found as YAML in conf/config.yaml and can be replaced by commandline specifications

python src/main.py sampling.num_steps=1000 student.epochs=10

To print the current configuration, run

python src/main.py --cfg job

Enable WandB logs:

python src/main.py env.wandb=true ...

Structure

• conf: Configuration files
• conf/experiment: Specific experiment configuration overrides
• src: Python code

Installation

We made sure to capture all version specific dependencies in requirements.txt:

pip install -r requirements.txt

Tested with Python 3.10.13.

Major Libraries

• PyTorch: Autograd and Networks
• Lightning: ML Pipeline
• timm: Vision models
• Hydra: Configuration
• WandB: Logging

Cite

@inproceedings{braun2024cake,
      title={Deep Classifier Mimicry without Data Access}, 
      author={Steven Braun and Martin Mundt and Kristian Kersting},
      year={2024},
      journal={International Conference on Artificial Intelligence and Statistics (AISTATS)}
}