This prompted me to build an easy way to generate synthetic data for machine learning models. This primarily uses GANs, but we use techniques which are most efficient for specific usecases.
Areas where we've found it useful are biomedical, drone imagery, satellite imagery, retail, and autonomous mobility.
As already prominent in the ImageNet challenge, the state of the art is using synthetic data to gain higher accuracy. [ https://paperswithcode.com/sota/image-classification-on-imagenet ]
Google, for their autonomous vehicles, used millions of miles of real driving data and billions of miles of synthetic data. It is clear where the world is moving towards.
I would be happy to share the tools with everyone since dealing with data is something we struggled with and don't want anyone to struggle anymore. This is probably only the first step towards building something robust that can reduce as much data hassles as possible, if not all.
