The challenge and opportunity for startups competing against incumbents with inherent data advantage is to leverage the best visual data with correct labels to train computers accurately for diverse use cases. Simulating data will level the playing field between large technology companies and startups.
Very intriguing. I just don’t understand this well enough to figure out why generated data doesn’t just take the assumptions that go into its own creation and pass them through as wanted bias into the system to be trained. I just don’t see how you get around that. Anyone have some insight here?