this post was submitted on 13 Apr 2024
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Supplementary synthetic data increases the quality of the model.
Correct. To a certain extend one can add AI data into AI, too much and you add noise, making the result worse, like a copy of a copy.
Yes, though that's not what they're doing. They train on images uploaded to their marketplace and, of course, some of these are AI generated.
It's fine as long as it's not the majority.
It doesn't really matter how much it is. An image is an image.
Data augmentation is a thing since a long time, but of course if the majority of your data is synthetic your model will suck on real world data. Though as these generative models get better and better at mimicking real world data and we select the results we want to use (removing the nonsense and hallucinations, artifacts etc.), we’re still feeding them “more data”.
I guess we’ll have to wait and see what effect it’ll produce on future models. I think overall the improvements on LLMs have been good, even at slow steps we’re still figuring out how to better turn them into useful tools. I don’t know how well the image generation models have improved in the last 2 years though.
Yes, that's one way of putting it. What gets into the Adobe stock database is already curated. They also have the sales and tracking data.
Also yes on this. It doesn't matter if your data is synthetic but only if it's fit for purpose. That's especially true in this case, where the distinction between synthetic and real is so unclear. You're already including drawings, renders, photomanips, etc. I have no idea what kind of misconception people have that they would think it matters if some piece of digital art is AI generated.
I'm just talking about synthetic images affect model quality.
It doesn't matter how the image was made. It only matters what it is like and how it is used to affect the model.
That's what I'm saying. Synthetic images can help your model look better, but if you're aiming for “realistic” output, but synthetic images are fundamentally not real images and too many will bias your model in a slightly different direction.