amm.pytorch

Code for our T-PAMI 2019 paper Adversarial Margin Maximization Networks.

Our paper is also available on arxiv.

Environments

• Python 3.5
• PyTorch 1.1.0
• torchvision 0.2.2
• glog 0.3.1

Datasets and Reference Models

We use MNIST, CIFAR-10/100, SVHN and ImageNet in pytorch's default format. One can check scripts in datasets/ for more details.

Usage

To train a MLP800 on MNIST using cross entropy loss:

python3 generalization.py --scratch --dataset mnist --arch mlp800 --use-trainval --lmbd 0 --lr 0.005 

To train a MLP800 on MNIST using AMM:

python3 generalization.py --d 1.0 --scratch --shrinkage exp --dataset mnist --c 2.0 --arch mlp800 --use-trainval --lmbd 32.0 --aggregation min --lr 0.005 

Citation

Please cite our work in your publications if it helps your research:

@article{yan2019adversarial,
  title={Adversarial Margin Maximization Networks},
  author={Yan, Ziang and Guo, Yiwen and Zhang, Changshui},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2019},
  publisher={IEEE}
}