TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

SotA/Adversarial/Adversarial Robustness/ImageNet-A

Adversarial Robustness on ImageNet-A

Metric: Accuracy (higher is better)

LeaderboardDataset
Loading chart...

Results

Submit a result
#Model↕Accuracy▼Extra DataPaperDate↕Code
1DeiT-S (AdamW, Cosine)12.2NoAre Transformers More Robust Than CNNs?2021-11-10Code
2ResNet-50 (SGD, Cosine)3.3NoAre Transformers More Robust Than CNNs?2021-11-10Code
3ResNet-50 (SGD, Step)3.2NoAre Transformers More Robust Than CNNs?2021-11-10Code
4ResNet-50 (AdamW, Cosine)3.1NoAre Transformers More Robust Than CNNs?2021-11-10Code