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Models/CTGAN

CTGAN

Reported on 26 benchmarks across 2 tasks · 3 papers · 7 SOTA

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

Miscellaneous24 results

Tabular Data GenerationonCalifornia Housing Prices
DT Mean Squared Error· 2019-07-01
0.82
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
0.61
best: 0.34 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonCalifornia Housing Prices
RF Mean Squared Error· 2019-07-01
0.62
best: 0.28 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
Parameters(M)· 2019-07-01
9.6
best: 355 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
LR Accuracy· 2019-07-01
83.2
best: 85.45 (Binary Diffusion)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
Parameters(M)· 2019-07-01
0.302
best: 355 (GReaT)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
RF Accuracy· 2019-07-01
83.53
best: 85.74 (Binary Diffusion)
SOTA
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonSICK
DT Accuracy· 2019-07-01
92.05
best: 97.72 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonSICK
LR Accuracy· 2019-07-01
94.44
best: 97.72 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonSICK
Parameters(M)· 2019-07-01
0.222
best: 355 (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
61.34
best: 81.4 (Distill-GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
LR Accuracy· 2019-07-01
57.72
best: 71.9 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
Parameters(M)· 2019-07-01
0.277
best: 355 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonHELOC
RF Accuracy· 2019-07-01
62.35
best: 82.14 (Distill-GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonCalifornia Housing Prices
Parameters(M)· 2019-07-01
0.197
best: 355 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonTravel
DT Accuracy· 2019-07-01
73.3
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
Parameters(M)· 2019-07-01
0.155
best: 355 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonTravel
RF Accuracy· 2019-07-01
71.41
best: 89.95 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
DT Accuracy· 2019-07-01
0.4973
best: 0.5713 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
LR Accuracy· 2019-07-01
0.5093
best: 0.5775 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonDiabetes
RF Accuracy· 2019-07-01
0.5223
best: 0.5834 (GReaT)
Modeling Tabular data using Conditional GAN arXiv:1907.00503
Tabular Data GenerationonAdult Census Income
DT Accuracy· 2019-07-01
81.32
best: 85.27 (Binary Diffusion)
Modeling Tabular data using Conditional GAN arXiv:1907.00503

Medical2 results

Synthetic Data GenerationonTitanic
AUC· 2024-10-28
0.7923
best: 0.8163 (zGAN)
zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation arXiv:2410.20808
Synthetic Data GenerationonUNSW-NB15
EMD· 2024-05-26
0.07
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation arXiv:2405.16476