robust-ml / robust-ml.github.io

A community-run reference for state-of-the-art adversarial example defenses.
https://www.robust-ml.org/
Creative Commons Attribution Share Alike 4.0 International
49 stars 7 forks source link

Max-Mahalanobis Training #10

Closed P2333 closed 5 years ago

P2333 commented 5 years ago

Name: {Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness }

Authors: {Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu}

Paper: {https://arxiv.org/pdf/1905.10626.pdf}

Code: { https://github.com/P2333/Max-Mahalanobis-Training }

Venue: {venue if published}

Does the code implement the robust-ml API and include pre-trained models: {no}

Dataset: {cifar10}

Threat model: {ℓ∞(ϵ=8/255)}

Natural accuracy: {93.6%}

Claims: {46.0%}