Tabular Data Generation on SICK
DT Accuracy
- 2019-07-01 — TVAE: DT Accuracy 95.39
- 2022-10-12 — GReaT: DT Accuracy 97.72
| Rank | Model | DT Accuracy | LR Accuracy | Parameters(M) | RF Accuracy | Paper | Code | Year |
|---|---|---|---|---|---|---|---|---|
| 1 | GReaT | 97.72 | 97.72 | 355 | 98.3 | Language Models are Realistic Tabular Data Generators | kathrinse/be_great | 2022 |
| 2 | Binary Diffusion | 97.07 | 96.14 | 1.4 | 96.59 | Tabular Data Generation using Binary Diffusion | vkinakh/binary-diffusion-tabular | 2024 |
| 3 | Distill-GReaT | 95.39 | 96.56 | 82 | 97.72 | Language Models are Realistic Tabular Data Generators | kathrinse/be_great | 2022 |
| 3 | TVAE | 95.39 | 94.7 | 0.046 | 94.91 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |
| 5 | CopulaGAN | 93.77 | 94.57 | 0.226 | 94.57 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |
| 6 | CTGAN | 92.05 | 94.44 | 0.222 | 94.57 | Modeling Tabular data using Conditional GAN | ydataai/ydata-synthetic · DAI-Lab/CTGAN · sdv-dev/CTGAN · +6 | 2019 |