[New Model] <Amini2024MeanSparse>

[mrteymoorian]

Paper Information

• Paper Title: MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification
• Paper URL: https://arxiv.org/pdf/2406.05927
• Paper authors: Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma, Amir Houmansadr

Leaderboard Claim(s)

Add here the claim for your model(s). Copy and paste the following subsection for the number of models you want to add.

Model 1

• Architecture: meansparse_ra_wrn70_16
• Dataset: CIFAR-10
• Threat Model: Linf
• eps: 8/255
• Clean accuracy: 93.24
• Robust accuracy: 72.08
• Additional data: true
• Evaluation method: AutoAttack
• Checkpoint and code: Checkpoint and code

Model 2

• Architecture: meansparse_ConvNeXt-L
• Dataset: ImageNet
• Threat Model: Linf
• eps: 4/255
• Clean accuracy: 77.96
• Robust accuracy: 59.64
• Additional data: false
• Evaluation method: AutoAttack
• Checkpoint and code: Checkpoint and code

Model Zoo:

• [x] I want to add my models to the Model Zoo (check if true)
• [x] I use an architecture that is included among those here or in timm. If not, I added the link to the architecture implementation so that it can be added.
• [x] I agree to release my model(s) under MIT license (check if true) OR under a custom license, located here: (put the custom license URL here if a custom license is needed. If no URL is specified, we assume that you are fine with MIT)

[fra31]

Hi,

thanks for the submission and PR, I'll add the models in the next few days.

[fra31]

I've integrated the models in https://github.com/RobustBench/robustbench/pull/185 (since anyway the Gdrive IDs needed to be updated). Please let me know if there's anything to modify.

[mrteymoorian]

Thank you so much. When can we expect our model's results to be seen on the robustbench website?

[fra31]

The leaderboards should be updated with https://github.com/RobustBench/robustbench.github.io/pull/15.