Gaussian approximation of continuous variables really clear in non-gaussian/non-multimodal data about ctgan HOT 5 OPEN

Hi, I'm not sure if I understand your plots correctly. Are these plots about the cumulative distribution for synthetic data (generated by GAN) and real (training) data?

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Both, orange is synthetic, blue is real. I'll upload some clearer plots tomorrow.

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This is a bit clearer with a distribution plot. For all plots: blue is real data of one column, orange is fake data of the same column. The fake data in this plot was generated with CTGAN.

For example, this was from data generated with TGAN:

And from my WGAN adaptation of TGAN:

So in these plots, we see a clear decrease in spikyness of the generated data. I'm trying to figure out what causes this, cause the data in the TGAN-WGAN is modelled quite well, while the data in CTGAN and TGAN is quite clearly several smaller distributions.

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@Baukebrenninkmeijer just to confirm, the TGAN-WGAN implementation you are talking about is https://github.com/Baukebrenninkmeijer/On-the-Generation-and-Evaluation-of-Synthetic-Tabular-Data-using-GANs/tree/master/tgan_wgan_gp?

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@shreyanshs Yes, that is correct. However, it's quite old now.

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