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Models/CopulaGAN

CopulaGAN

Reported on 25 benchmarks across 2 tasks · 2 papers · 6 SOTA

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

Miscellaneous24 results

Tabular Data GenerationonSICK
Parameters(M)· 2019-07-01
0.226
best: 355 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonCalifornia Housing Prices
DT Mean Squared Error· 2019-07-01
1.19
best: 0.39 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonCalifornia Housing Prices
LR Mean Squared Error· 2019-07-01
0.98
best: 0.34 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonCalifornia Housing Prices
Parameters(M)· 2019-07-01
0.201
best: 355 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonCalifornia Housing Prices
RF Mean Squared Error· 2019-07-01
0.99
best: 0.28 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonTravel
Parameters(M)· 2019-07-01
0.157
best: 355 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonSICK
DT Accuracy· 2019-07-01
93.77
best: 97.72 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonSICK
LR Accuracy· 2019-07-01
94.57
best: 97.72 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonSICK
RF Accuracy· 2019-07-01
94.57
best: 98.3 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
DT Accuracy· 2019-07-01
42.36
best: 81.4 (Distill-GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
LR Accuracy· 2019-07-01
42.03
best: 71.9 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
Parameters(M)· 2019-07-01
0.276
best: 355 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
RF Accuracy· 2019-07-01
42.35
best: 82.14 (Distill-GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonTravel
DT Accuracy· 2019-07-01
73.61
best: 88.9 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonTravel
LR Accuracy· 2019-07-01
73.3
best: 83.79 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonTravel
RF Accuracy· 2019-07-01
73.3
best: 89.95 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
DT Accuracy· 2019-07-01
0.385
best: 0.5713 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
LR Accuracy· 2019-07-01
0.4027
best: 0.5775 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
Parameters(M)· 2019-07-01
9.4
best: 355 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
RF Accuracy· 2019-07-01
0.3759
best: 0.5834 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
DT Accuracy· 2019-07-01
76.29
best: 85.27 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
LR Accuracy· 2019-07-01
80.61
best: 85.45 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
Parameters(M)· 2019-07-01
0.3
best: 355 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
RF Accuracy· 2019-07-01
80.46
best: 85.74 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503

Medical1 result

Synthetic Data GenerationonTitanic
AUC· 2024-10-28
0.8076
best: 0.8163 (zGAN)
zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation arXiv:2410.20808