• Introduces NoGAN, a faster alternative to traditional tabular data synthetization.
• Runs 1000x quicker than GAN, delivering superior results with a new, sophisticated evaluation metric.
• A significant cost reducer, minimizing cloud/GPU time and training time.
• Replaces manual fine-tuning parameters with auto-tuning.
• Now available as open-source software.
• Real-life case studies: synthetization in <5 seconds (compared to 10 minutes with GAN).
• Produces higher quality results, confirmed via cross-validation.
• Fast implementation enables automatic, efficient hyperparameter fine-tuning.
• Future improvements discussed: speed enhancement, data faithfulness, auto-tuning, Gaussian NoGAN, and broader applications.

https://docs.google.com/presentation/d/1kDlAhS8yh_-Yu19ICxFk0Hxfq3ZXc4iy/mobilepresent?slide=id.p1