Closed iCGY96 closed 3 years ago
Hi,
thanks for the submission! I'd add your model, with the evaluation on the full test set, to RobustBench since it is currently up-to-date, if this is fine for you.
Very Thanks.
Added with https://github.com/RobustBench/robustbench/commit/a97f10d6523017f547fb4a181b7933e54c8eb46b. If it looks fine to you, I update the website too.
Thank you for your evaluation. RobustBench is a very good platform. We are not going to add the current results to RobustBench now. We will submit more results uniformly when we are ready for more models about cifar10-c and cifar100-c.
Paper: Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
Venue: ICCV 2021
Dataset and threat model: CIFAR-10, L-inf, 8/255
Code: code
Pre-trained model: model
Log file: log
Additional data: no
Clean and robust accuracy: 84.30% / 45.70%
Architecture: RN-18
Description of the model/defense: Motivated by the powerful generalizability of the human, we argue that reducing the dependence on the amplitude spectrum and enhancing the ability to capture phase spectrum can improve the robustness of CNN.