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Models/DiscreteViT

DiscreteViT

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Methodology3 results

Domain AdaptationonStylized-ImageNet
Top 1 Accuracy· 2021-11-20
22.19
best: 32.77 (MAE+DAT (ViT-H))
SOTA
Discrete Representations Strengthen Vision Transformer Robustness arXiv:2111.10493
Domain AdaptationonImageNet-R
Top-1 Error Rate· 2021-11-20
44.74
best: 3.9 (Model soups (BASIC-L))
Discrete Representations Strengthen Vision Transformer Robustness arXiv:2111.10493
Domain AdaptationonImageNet-C
mean Corruption Error (mCE)· 2021-11-20
46.22
best: 22 (EfficientNet-L2+RPL)
Discrete Representations Strengthen Vision Transformer Robustness arXiv:2111.10493

Computer Vision3 results

Domain GeneralizationonStylized-ImageNet
Top 1 Accuracy· 2021-11-20
22.19
best: 32.77 (MAE+DAT (ViT-H))
SOTA
Discrete Representations Strengthen Vision Transformer Robustness arXiv:2111.10493
Domain GeneralizationonImageNet-R
Top-1 Error Rate· 2021-11-20
44.74
best: 3.9 (Model soups (BASIC-L))
Discrete Representations Strengthen Vision Transformer Robustness arXiv:2111.10493
Domain GeneralizationonImageNet-C
mean Corruption Error (mCE)· 2021-11-20
46.22
best: 28.2 (DINOv2 (ViT-g/14, frozen model, linear eval))
Discrete Representations Strengthen Vision Transformer Robustness arXiv:2111.10493