DRAUC

This is a PyTorch implementation of DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework.

Environments

• torch>=1.5.0
• torchvision>=0.6.0
• scikit-learn
• pillow

Data Preparation

Download following datasets:

• CIFAR-10 and CIFAR-100 from https://www.cs.toronto.edu/~kriz/cifar.html
• MNIST from http://yann.lecun.com/exdb/mnist/
• Tiny-ImageNet from https://huggingface.co/datasets/zh-plus/tiny-imagenet
• CIFAR-10-C from https://zenodo.org/records/2535967
• CIFAR-100-C from https://zenodo.org/records/3555552
• MNIST-C from https://zenodo.org/records/3239543
• Tiny-ImageNet-C from https://zenodo.org/records/2469796

Preprocess dataset: run split_train_valid.py to generate train valid split for Tiny-ImageNet

$ tree /path/to/your/datasets/
  ├── CIFAR10
  │   └── cifar-10-batches-py
  │       ├── batches.meta
  │       ├── data_batch_1
  │       ├── ...
  ├── CIFAR100
  │   └── cifar-100-python
  │       ├── cifar-100-python.tar.gz
  │       ├── file.txt~
  │       ├── meta
  │       ├── test
  │       └── train
  ├── CIFAR-100-C
  │   ├── brightness.npy
  │   ├── contrast.npy
  │   ├── ...
  ├── CIFAR-10-C
  │   ├── brightness.npy
  │   ├── contrast.npy
  │   ├── ...
  ├── MNIST
  │   ├── ImbalancedMNIST
  │   │   ├── processed
  │   │   └── raw
  ├── MNIST-C
  │   ├── brightness
  │   │   ├── test_images.npy
  │   │   ├── test_labels.npy
  │   │   ├── train_images.npy
  │   │   └── train_labels.npy
  │   ├── canny_edges
  │   │   ├── test_images.npy
  │   │   ├── ...
  |   ├── ...
  ├── TINYIMAGENET-H
  │   ├── test
  │   │   ├── n01443537
  │   │   ├── n01629819
  │   │   ├── ...
  │   ├── train
  │   │   ├── n01443537
  │   │   ├── n01629819
  │   │   ├── ...
  │   ├── valid
  │   │   ├── n01443537
  │   │   ├── n01629819
  │   │   ├── ...
  │   ├── wnids.txt
  │   └── words.txt
  ├── TINYIMAGENET-C
      ├── brightness
      ├── contrast
      ├── ...
  

Training

python run.py

Citation

If you use the code of this repository, please cite our paper:

@article{dai2023drauc,
      title={DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework}, 
      author={Siran Dai and Qianqian Xu and Zhiyong Yang and Xiaochun Cao and Qingming Huang},
      year={2023},
      eprint={2311.03055},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.