5 ms·
If you use computer generated images to teach your networks, you have a nice network for computer generated images. It might be good to pre-train a net (instead
by hansdieter1337 7y ago
If you use computer generated images to teach your networks, you have a nice network for computer generated images. It might be good to pre-train a net (instead of random weights), but you still need labeling of real world images. E.g., a guy once trained a self driving car model in GTA 5. I’m sure this algorithm won’t do great in the real world.
But I already see an industry forming, promising to get rid of all labeled data. And there will be idiots believing it. That’s how all the Silicon Valley startups work. Unicorns on Powerpoint slides and an empty basket in reality. (src: living and working in the valley)
- cbrun 7y agoI read about the guy who used GTA to train a neural net. I think he was trying to make the point that although obviously imperfect, using simulated data could actually work. I'm not saying using simulated data (SD) should the be-all end-all for training neural nets, but we're seeing algorithms performing better when they're trained using a combination of real labelled data and SD rather than real data alone. I hear your point though about hype cycles and the tunnel vision SV can often fall into.