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From reading the article it looks like the first step they'd call for is to retrain the model to line up with reality? If 34% of Judges are female you'd expect

From reading the article it looks like the first step they'd call for is to retrain the model to line up with reality? If 34% of Judges are female you'd expect 34% of the generated images to be perceived as female. Inspiration would be skewing it in the other direction

RugnirViking 3y ago
> If 34% of Judges are female you'd expect 34% of the generated images to be perceived as female. That's not what i'd expect, no. I'd expect as it trains it would begin to trend towards a median idea of a single judge, an ur-judge if you will. It will represent the median judge. And then with fine-tuning training it would stop making ambiguous characteristics, which would lead it down the most likely path every time. You'd have to intentionally inject some randomness to prevent this. If anything i'm a little surprised they got as much diversity as they did - I can only assume there is some light randomness applied behind the scenes. Note I'm not commenting on "should", but rather what would likely happen given an training dataset that reflects reality
- vikramkr 3y ago
  Something that behaves like that would be a useless model. These models are not deterministic, llms for example have a literal temperature dial you can turn to increase randomness. If it narrows onto the idea of a single judge then what's the point of the AI? You could just grab a picture of some old white guy (your median judge) and use Photoshop and call it a day. Or just use stock photos if all the generated judges are the same.
LudwigNagasena 3y ago
If there is >50% of something, I wouldn't be surprised if a model chooses it 100% of time.
- vikramkr 3y ago
  I know this is an old thread but also just remembered - what folks are describing in this thread (where it just picks the mode) is actually explicitly one of the reasons we use diffusion instead of GANs - it's called mode collapse and it's something people spend a lot of time and effort trying to prevent: https://proceedings.neurips.cc/paper/2017/file/44a2e0804995faf8d2e3b084a1e2db1d-Paper.pdf https://proceedings.neurips.cc/paper/2017/file/44a2e0804995f...
- vikramkr 3y ago
  I"m really confused by all these responses in this vein. If the model just chooses the most likely thing 100% of the time that's an entirely useless piece of software! Just use a stock photo or a bar chart and pick the mode!
  - LudwigNagasena 3y ago
    Whether it is useless or not depends on your needs.
    - vikramkr 3y ago
      No because if it always produces a deterministic image it's just lookup table. The whole point is that it's generative ai, not a way to burn all the computational power to generate some summary statistics

RugnirViking 3y ago

> If 34% of Judges are female you'd expect 34% of the generated images to be perceived as female. That's not what i'd expect, no. I'd expect as it trains it would begin to trend towards a median idea of a single judge, an ur-judge if you will. It will represent the median judge. And then with fine-tuning training it would stop making ambiguous characteristics, which would lead it down the most likely path every time. You'd have to intentionally inject some randomness to prevent this. If anything i'm a little surprised they got as much diversity as they did - I can only assume there is some light randomness applied behind the scenes. Note I'm not commenting on "should", but rather what would likely happen given an training dataset that reflects reality

vikramkr 3y ago
Something that behaves like that would be a useless model. These models are not deterministic, llms for example have a literal temperature dial you can turn to increase randomness. If it narrows onto the idea of a single judge then what's the point of the AI? You could just grab a picture of some old white guy (your median judge) and use Photoshop and call it a day. Or just use stock photos if all the generated judges are the same.

LudwigNagasena 3y ago

If there is >50% of something, I wouldn't be surprised if a model chooses it 100% of time.

vikramkr 3y ago
I know this is an old thread but also just remembered - what folks are describing in this thread (where it just picks the mode) is actually explicitly one of the reasons we use diffusion instead of GANs - it's called mode collapse and it's something people spend a lot of time and effort trying to prevent: https://proceedings.neurips.cc/paper/2017/file/44a2e0804995faf8d2e3b084a1e2db1d-Paper.pdf https://proceedings.neurips.cc/paper/2017/file/44a2e0804995f...
vikramkr 3y ago
I"m really confused by all these responses in this vein. If the model just chooses the most likely thing 100% of the time that's an entirely useless piece of software! Just use a stock photo or a bar chart and pick the mode!
- LudwigNagasena 3y ago
  Whether it is useless or not depends on your needs.
  - vikramkr 3y ago
    No because if it always produces a deterministic image it's just lookup table. The whole point is that it's generative ai, not a way to burn all the computational power to generate some summary statistics