CARD: Classification and Regression Diffusion Models

This repo contains the official implementation for the paper CARD: Classification and Regression Diffusion Models by Xizewen Han, Huangjie Zheng, and Mingyuan Zhou. Published in NeurIPS 2022 (poster).

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How to Run the Code

Dependencies

We recommend configuring the environment through conda. Run the following to install and to activate the environment

conda env create -f environment.yml
conda activate card

The name of the environment is set to card by default. You can modify the first line of the environment.yml file to set the new environment's name.

Usage

We organize our code by the type of tasks into the corresponding regression and classification directories. As we assume the response variable $\boldsymbol{y}$ to reside in the real continuous space for both regression and classification tasks, the code for training and inference of the diffusion models are the same (diffusion_utils.py file). The main differences are the handling of the $\boldsymbol{f_{\phi}}$ model (named self.cond_pred_model in both card_regression.py and card_classification.py), the evaluation of trained models (class function test), and the neural network architecture (model.py file).

We provide the scripts of model training and evaluation for the tasks reported in our paper in the training_scripts directory, including:

• Regression
  • 8 toy examples: linear regression, quadratic regression, sinusoidal regression, log-log linear regression, log-log cubic regression, inverse sinusoidal regression, 8 Gaussians, full circle
  • 10 UCI tasks: Boston Housing, Concrete Strength, Energy Efficiency, Kin8nm, Naval Propulsion, Power Plant, Protein Structure, Wine Quality Red, Yacht Hydrodynamics, Year Prediction MSD
• Classification
  • CIFAR-10, CIFAR-100, ImageNet, FashionMNIST, MNIST (noisy)

Note that for the UCI regression tasks, all data and the corresponding split schemes are adapted through the official repo of MC Dropout, except the YearPredictionMSD dataset due to its size. A link to the full UCI dataset that we used for our experiments can be found here for download.

We provide the following example to run the model on the Boston Housing regression task:

bash training_scripts/run_uci_boston.sh

The configuration for each of the above listed tasks (including data file location, training log and evaluation result directory settings, neural network architecture, optimization hyperparameters, etc.) are provided in the corresponding files in the configs directory. For each experimental run, you can find within the following 4 directories:

• logs: stdout.txt with training logs, testmetrics.txt with evaluation metrics
• tensorboard: files to track training progress
• training_image_samples: plots during training
• testing_image_samples: plots when evaluating the model

Checkpoints

The checkpoints of all 10 UCI regression tasks and all image classification tasks have been uploaded to Google Drive, and can be accessed and downloaded here.

References

If you find the code helpful for your research, please consider citing

@inproceedings{han2022card,
  title={CARD: Classification and Regression Diffusion Models},
  author={Han, Xizewen and Zheng, Huangjie and Zhou, Mingyuan},
  booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
  year={2022}
}