7 ms·

Good article and great tech. However, I don't know if I believe the results are as good as they claim. Many of the pictures look a bit off to me, like they al

by oillio 9y ago

Good article and great tech. However, I don't know if I believe the results are as good as they claim. Many of the pictures look a bit off to me, like they all have dead eyes. Maybe celebrities generally look like that anyway, so it is being true to form. :) In particular, I think this guy is missing a pretty significant part of his head: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e890dbb781d60b011c9be1f9a169ed2583a0dda/finished/7.png https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...

atourgates 9y ago
The hair was the giveaway to me. I stared at the two "which one is real" images for a couple minutes thinking, "They both have that fake looking wavy hair, I thought for sure neither was real." Cheap trick NYTimes. Cheap.
- nerfhammer 9y ago
  look at their foreheads, there's still like blurry half-generated wavey hair texture on their skin.
GuB-42 9y ago
In most cases hair gave it away for me, especially at the contours. The picture you linked is an extreme example. However, give it to a good Photoshop artist, like most celebrity pictures are, and I'm sure these issues will be fixed in no time.
visarga 9y ago
The inventor of GANs, which have been considered the most interesting idea in ML in the last decade, is Ian Goodfellow. I met him on reddit a few years ago. I was supposed to get private ML tutoring from him, just around the time Andrew Ng opened the first Coursera course. I didn't get lessons because I gave up and eventually took the MOOC. But it's amazing to know we share the same forums and sometimes exchange a comment or two. The great idea about GANs is that they replace one of the most hard to understand parts of a neural net - the "Loss function" - with another neural net, thus making the loss function learnable. This opens up the door for a kind of unsupervised learning that was impossible to make work before. GANs are very very important also because they are almost like reinforcement learning (actor + critic = RL, generator + discriminator = GAN), and RL is supposed to be the way to AGI. The most famous problem of GANs is instability during training and mode collapse - which is like a student learning especially for an exam (and not in general) thus optimising for the test instead of the real thing.
- amenod 9y ago
  > The most famous problem of GANs is instability during training and mode collapse - which is like a student learning especially for an exam (and not in general) thus optimising for the test instead of the real thing. I must confess I haven't worker with GANs yet, but isn't that the whole point of GANs? Student is optimising for the test while the teacher is learning how to make tests as similar to reality as possible? If I understand correctly, the main challenge is finding a way to allow teacher and student (well, generator and adversary) to learn at a similar rate, so that one doesn't stop learning because its competitor is too advanced. Is that correct?
  - vinn124 9y ago
    > but isn't that the whole point of GANs? not quite, but youre on the right path. think about it this way: you (the generative model) are trying to predict a unit gaussian, which is just a fancy way to say bell curve. you get +1 if you predict a number in this distribution (eg 0.1 or -0.5, which is within one standard deviation of the mean of 0); you get -1 if you predict a number thats "far" from this distribution (something like 40 - which has an infinitesimally low probability of being drawn from a unit gaussian). mode collapse, then, is when you predict 0 all the time. yes, you are technically correct but youve failed to learn the true distribution. obviously ive simplified this quite a bit and have anthropomorphize the model, but i hope you get the gist. otherwise, the [original paper](https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661) is refreshingly easy to read.
    - amenod 9y ago
      Thanks!
- claytonjy 9y ago
  > RL is supposed to be the way to AGI Could you expand on that? The more I read from folks like LeCunn & Chollet seem to disagree strongly. Just this week Yan posted about unsupervised modeling (with or without DL) to be the next path forward, and described RL as essentially a roundabout way of doing supervised learning.
delecti 9y ago
You were primed to be looking for flaws by the nature of the article. It wouldn't be hard to come up with a context where each and every one of the 3x3 grid of pictures in that article was accepted at face value.
- Retric 9y ago
  They might work as thumbnails, but these are terrible when blown up to full size. When given both images I was trying to find one that might be real thinking it could be some freaky filter or something. And I still had a 'these are terrible fakes feeling.' Even the 'best' headline image fails as the eyes are not the same size and the rest of the face just looks off.
  - delecti 9y ago
    Did you even read my comment? They're not perfect, but you were expecting them to be fake. Someone not told there would be computer generated images would be considerably easier to fool. Also, probably the bigger risk is not that you'll be shown an entirely fabricated image, but rather that someone could convincingly be inserted into an existing image.
    - Retric 9y ago
      I was not thinking about fake images when looking at the article this was pure instinctive revulsion. It's easier to avoid the uncanny valley with pictures than motion, but some of theses fall deep into it and many others don't even make it that far.
tboyd47 9y ago
Even better: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e890dbb781d60b011c9be1f9a169ed2583a0dda/finished/5.png https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e... That's one heck of a receding hairline, meaning receding out of the plane of existence.
amenod 9y ago
And the ears are not "compatible" either. Could this be the reason they prefer women's faces (with long hair which covers ears) in the article?
ouid 9y ago
The test of believability they give in the article is also bullshit. Both of the options are fakes, they both even look like fakes. Her hair and forehead doesn't make sense, his mouth and ears don't make sense. They don't match it up against a real picture because people's performance on that task would contradict the headline.
- vinn124 9y ago
  > The test of believability yep! that is the fundamental limitation of adversarial networks. theres no good measure or "loss", as it's highly subjective.
alergico 9y ago
Most likes I ever got on OKCupid was when I used a GAN-generated celebrity as my profile picture. Just one girl noticed something wasn't quite right.
yummybear 9y ago
Several of them seem to be using many features from specific celebrities. It may just be me, but there is a very strong similarity to Paul Walker, Liv Tyler, Michael Douglas and Adam Sandler in some of these. I don't know if it's a result of overfitting?
chuckdries 9y ago
https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e890dbb781d60b011c9be1f9a169ed2583a0dda/finished/5.png https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...
unsined 9y ago
To me the fine details are incongruent: the grain of the hair sporadically changes direction, patches of skin have different qualities. It looks like a bit of Frankenstein's work.

by oillio 9y ago

atourgates 9y ago

The hair was the giveaway to me. I stared at the two "which one is real" images for a couple minutes thinking, "They both have that fake looking wavy hair, I thought for sure neither was real." Cheap trick NYTimes. Cheap.

nerfhammer 9y ago
look at their foreheads, there's still like blurry half-generated wavey hair texture on their skin.

GuB-42 9y ago

In most cases hair gave it away for me, especially at the contours. The picture you linked is an extreme example. However, give it to a good Photoshop artist, like most celebrity pictures are, and I'm sure these issues will be fixed in no time.

visarga 9y ago

The inventor of GANs, which have been considered the most interesting idea in ML in the last decade, is Ian Goodfellow. I met him on reddit a few years ago. I was supposed to get private ML tutoring from him, just around the time Andrew Ng opened the first Coursera course. I didn't get lessons because I gave up and eventually took the MOOC. But it's amazing to know we share the same forums and sometimes exchange a comment or two. The great idea about GANs is that they replace one of the most hard to understand parts of a neural net - the "Loss function" - with another neural net, thus making the loss function learnable. This opens up the door for a kind of unsupervised learning that was impossible to make work before. GANs are very very important also because they are almost like reinforcement learning (actor + critic = RL, generator + discriminator = GAN), and RL is supposed to be the way to AGI. The most famous problem of GANs is instability during training and mode collapse - which is like a student learning especially for an exam (and not in general) thus optimising for the test instead of the real thing.

amenod 9y ago
> The most famous problem of GANs is instability during training and mode collapse - which is like a student learning especially for an exam (and not in general) thus optimising for the test instead of the real thing. I must confess I haven't worker with GANs yet, but isn't that the whole point of GANs? Student is optimising for the test while the teacher is learning how to make tests as similar to reality as possible? If I understand correctly, the main challenge is finding a way to allow teacher and student (well, generator and adversary) to learn at a similar rate, so that one doesn't stop learning because its competitor is too advanced. Is that correct?
- vinn124 9y ago
  > but isn't that the whole point of GANs? not quite, but youre on the right path. think about it this way: you (the generative model) are trying to predict a unit gaussian, which is just a fancy way to say bell curve. you get +1 if you predict a number in this distribution (eg 0.1 or -0.5, which is within one standard deviation of the mean of 0); you get -1 if you predict a number thats "far" from this distribution (something like 40 - which has an infinitesimally low probability of being drawn from a unit gaussian). mode collapse, then, is when you predict 0 all the time. yes, you are technically correct but youve failed to learn the true distribution. obviously ive simplified this quite a bit and have anthropomorphize the model, but i hope you get the gist. otherwise, the [original paper](https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661) is refreshingly easy to read.
  - amenod 9y ago
    Thanks!
claytonjy 9y ago
> RL is supposed to be the way to AGI Could you expand on that? The more I read from folks like LeCunn & Chollet seem to disagree strongly. Just this week Yan posted about unsupervised modeling (with or without DL) to be the next path forward, and described RL as essentially a roundabout way of doing supervised learning.

delecti 9y ago

You were primed to be looking for flaws by the nature of the article. It wouldn't be hard to come up with a context where each and every one of the 3x3 grid of pictures in that article was accepted at face value.

Retric 9y ago
They might work as thumbnails, but these are terrible when blown up to full size. When given both images I was trying to find one that might be real thinking it could be some freaky filter or something. And I still had a 'these are terrible fakes feeling.' Even the 'best' headline image fails as the eyes are not the same size and the rest of the face just looks off.
- delecti 9y ago
  Did you even read my comment? They're not perfect, but you were expecting them to be fake. Someone not told there would be computer generated images would be considerably easier to fool. Also, probably the bigger risk is not that you'll be shown an entirely fabricated image, but rather that someone could convincingly be inserted into an existing image.
  - Retric 9y ago
    I was not thinking about fake images when looking at the article this was pure instinctive revulsion. It's easier to avoid the uncanny valley with pictures than motion, but some of theses fall deep into it and many others don't even make it that far.

tboyd47 9y ago

Even better: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e890dbb781d60b011c9be1f9a169ed2583a0dda/finished/5.png https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e... That's one heck of a receding hairline, meaning receding out of the plane of existence.

amenod 9y ago

And the ears are not "compatible" either. Could this be the reason they prefer women's faces (with long hair which covers ears) in the article?

ouid 9y ago

The test of believability they give in the article is also bullshit. Both of the options are fakes, they both even look like fakes. Her hair and forehead doesn't make sense, his mouth and ears don't make sense. They don't match it up against a real picture because people's performance on that task would contradict the headline.

vinn124 9y ago
> The test of believability yep! that is the fundamental limitation of adversarial networks. theres no good measure or "loss", as it's highly subjective.

alergico 9y ago

Most likes I ever got on OKCupid was when I used a GAN-generated celebrity as my profile picture. Just one girl noticed something wasn't quite right.

yummybear 9y ago

Several of them seem to be using many features from specific celebrities. It may just be me, but there is a very strong similarity to Paul Walker, Liv Tyler, Michael Douglas and Adam Sandler in some of these. I don't know if it's a result of overfitting?

chuckdries 9y ago

https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e890dbb781d60b011c9be1f9a169ed2583a0dda/finished/5.png https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...

unsined 9y ago

To me the fine details are incongruent: the grain of the hair sporadically changes direction, patches of skin have different qualities. It looks like a bit of Frankenstein's work.