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Models/Xu et al.

Xu et al.

Reported on 6 benchmarks across 4 tasks · 2 papers · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Adversarial3 results

Adversarial AttackonCIFAR-10
Attack: AutoAttack· 2021-05-19
44.15
best: 78.13 (3-ensemble of multi-resolution self-ensembles)
SOTA
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks arXiv:2105.09109
Adversarial AttackonCIFAR-10
Attack: DeepFool· 2021-05-19
51.31
SOTA
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks arXiv:2105.09109
Adversarial AttackonCIFAR-10
Attack: PGD20· 2021-05-19
78.68
SOTA
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks arXiv:2105.09109

Computer Vision1 result

Depth EstimationonNYU-Depth V2
RMSE· 2017-04-07
0.586
best: 0.013 (Defocus/DepthNet (Normalized))
SOTA
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation arXiv:1704.02157

Methodology1 result

3DonNYU-Depth V2
RMSE· 2017-04-07
0.586
best: 0.013 (Defocus/DepthNet (Normalized))
SOTA
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation arXiv:1704.02157

Natural Language Processing1 result

CCG SupertaggingonCCGbank
Accuracy
93
best: 96.29 (Heterogeneous Dynamic Convolutions)