Add Backward Smoothing · Issue #26 · fra31/auto-attack (original) (raw)

Paper: Efficient Robust Training via Backward Smoothing https://arxiv.org/abs/2010.01278

Venue: {if applicable, the venue where the paper appeared}

Dataset and threat model: CIFAR-10/CIFAR100, Linf, 8/255

Code: https://github.com/jinghuichen/AutoAttackEval

Pre-trained model: https://drive.google.com/file/d/1lvMa2rbMrIVkAqsyrs_YXLBhewZBfdkP/view?usp=sharing (CIFAR10)
https://drive.google.com/file/d/1xNhK4w5ZuUSfbD_WR4xFKTprojaVux1A/view?usp=sharing (CIFAR100)

Log file: {link to log file of the evaluation}

Additional data: no

Clean and robust accuracy: CIFAR10 clean 85.32 robust 54.94 CIFAR100 clean 62.15 robust 31.92

Architecture: {wideresnet-34-10}

Description of the model/defense: Efficient robust training via backward smoothing

Thanks