[New Model] <Singh2023Revisiting>

[nmndeep]

Paper Information

• Paper Title: Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
• Paper URL: https://arxiv.org/pdf/2303.01870.pdf
• Paper authors: Naman D Singh, Francesco Croce, Matthias Hein

Leaderboard Claim(s)

Model 1

• Architecture: ViT-S-CvSt
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 72.56%
• Robust accuracy: 48.08%
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: checkpoint code Log

Model 2

• Architecture: ViT-B-CvSt
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 76.30%
• Robust accuracy: 54.66%
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: checkpoint code Log

Model 3

• Architecture: ConvNext-T-CvSt
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 72.72%
• Robust accuracy: 49.46%
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: checkpoint code Log

Model 4

• Architecture: ConvNext-S-CvSt
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 74.10%
• Robust accuracy: 52.42%
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: checkpoint code Log

Model 5

• Architecture: ConvNext-B-CvSt
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 75.90%
• Robust accuracy: 56.14%
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: checkpoint code Log

Model 6

• Architecture: ConvNext-L-CvSt
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 77.00%
• Robust accuracy: 57.70%
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: checkpoint code Log

Model Zoo:

• [x] I want to add my models to the Model Zoo (check if true)
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[fra31]

Added with https://github.com/RobustBench/robustbench/pull/134.