PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, Huawei Shen, Xueqi Cheng

The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024

This is an official PyTorch implementation of paper PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion.

Training & Testing

All arguments are located in the parse.py file. You can create a script to specify the parameters.

For example, you can run our PDE+ by using the following command:

bash ./scripts/train/pdeadd_cifar10.sh 

Or you can run the basic ERM by using the command:

bash ./scripts/train/std_cifar10.sh 

Full Results

All detailed experimental results, formatted as CSV files, are available in the results directory

Reference

If you find our work useful, please consider citing our paper:

@article{yuan2023pde+,
  title={PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion},
  author={Yuan, Yige and Xu, Bingbing and Lin, Bo and Hou, Liang and Sun, Fei and Shen, Huawei and Cheng, Xueqi},
  journal={arXiv preprint arXiv:2305.15835},
  year={2023}
}