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The training data contains examples of very dark skin: https://github.com/NVlabs/ffhq-dataset/blob/master/ffhq-teaser.png https://github.com/NVlabs/ffhq-dataset

The training data contains examples of very dark skin: https://github.com/NVlabs/ffhq-dataset/blob/master/ffhq-teaser.png https://github.com/NVlabs/ffhq-dataset/blob/master/ffhq-teas...

OscarCunningham 7y ago
Interesting. I just refreshed the site 100 times to get a large sample size and the darkest skinned image it produced was this girl (https://i.imgur.com/ggJIG94.png https://i.imgur.com/ggJIG94.png). Her skin is much lighter than many of the people in that sample of the training data. Assuming that that sample is representative of all the training data, it must be that the algorithm is 'choosing' not to produce images of dark skinned people. Perhaps because it isn't very good at them and 'knows' this? I wonder if there are other features it's deliberately avoiding?
- iamnothere 7y ago
  > Assuming that that sample is representative of all the training data, it must be that the algorithm is 'choosing' not to produce images of dark skinned people. Perhaps because it isn't very good at them and 'knows' this? I wonder if there are other features it's deliberately avoiding? An algorithm would not have this kind of insight. (This is just a GAN trained on face data.) To say that an algorithm like this is "deliberately" doing anything is a misunderstanding. Most generators like this that I've seen, like GPT-2, require you to provide a "seed" of some sort, such as a sentence fragment. They then build off of this seed. I don't know if this is implemented that way, but if so, perhaps the developer provided a set of seed data that leads to this result. There may also be a sort of "averaging" involved (see another commenter's note about the women having similar features), and depending on both the training data and the seed, this may result in a preference towards certain features. Edit: it's StyleGAN.
- goodside 7y ago
  You can see several examples of dark-skinned outputs around timestamp 2:41 in this video: https://youtu.be/c-NJtV9Jvp0 https://youtu.be/c-NJtV9Jvp0

The training data contains examples of very dark skin: https://github.com/NVlabs/ffhq-dataset/blob/master/ffhq-teaser.png https://github.com/NVlabs/ffhq-dataset/blob/master/ffhq-teas...

OscarCunningham 7y ago

Interesting. I just refreshed the site 100 times to get a large sample size and the darkest skinned image it produced was this girl (https://i.imgur.com/ggJIG94.png https://i.imgur.com/ggJIG94.png). Her skin is much lighter than many of the people in that sample of the training data. Assuming that that sample is representative of all the training data, it must be that the algorithm is 'choosing' not to produce images of dark skinned people. Perhaps because it isn't very good at them and 'knows' this? I wonder if there are other features it's deliberately avoiding?

iamnothere 7y ago
> Assuming that that sample is representative of all the training data, it must be that the algorithm is 'choosing' not to produce images of dark skinned people. Perhaps because it isn't very good at them and 'knows' this? I wonder if there are other features it's deliberately avoiding? An algorithm would not have this kind of insight. (This is just a GAN trained on face data.) To say that an algorithm like this is "deliberately" doing anything is a misunderstanding. Most generators like this that I've seen, like GPT-2, require you to provide a "seed" of some sort, such as a sentence fragment. They then build off of this seed. I don't know if this is implemented that way, but if so, perhaps the developer provided a set of seed data that leads to this result. There may also be a sort of "averaging" involved (see another commenter's note about the women having similar features), and depending on both the training data and the seed, this may result in a preference towards certain features. Edit: it's StyleGAN.
goodside 7y ago
You can see several examples of dark-skinned outputs around timestamp 2:41 in this video: https://youtu.be/c-NJtV9Jvp0 https://youtu.be/c-NJtV9Jvp0