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Models/CDAN

CDAN

Reported on 6 benchmarks across 2 tasks · 2 papers · 3 SOTA

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

Methodology3 results

Domain AdaptationonUSPS-to-MNIST
Accuracy· 2017-05-26
98
best: 98.75 (FAMCD)
SOTA
Conditional Adversarial Domain Adaptation arXiv:1705.10667
Domain AdaptationonSVHN-to-MNIST
Accuracy· 2017-05-26
89.2
best: 99.18 (Mean teacher)
SOTA
Conditional Adversarial Domain Adaptation arXiv:1705.10667
Domain AdaptationonVisDA2017
Accuracy· 2017-05-26
73.7
best: 93.8 (FFTAT)
SOTA
Conditional Adversarial Domain Adaptation arXiv:1705.10667

Computer Vision3 results

Image EnhancementonLOL
Average PSNR· 2023-08-24
20.102
best: 29.185 (CFWD)
CDAN: Convolutional dense attention-guided network for low-light image enhancement arXiv:2308.12902
Image EnhancementonLOL
LPIPS· 2023-08-24
0.167
best: 0.069 (BEM_)
CDAN: Convolutional dense attention-guided network for low-light image enhancement arXiv:2308.12902
Image EnhancementonLOL
SSIM· 2023-08-24
0.816
best: 0.93 (PyDiff)
CDAN: Convolutional dense attention-guided network for low-light image enhancement arXiv:2308.12902