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How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

nytimes.com

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Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#71

Post by gwern »

sago wrote: post #38

I don't understand the images associated with that article. They purport to show the progressive refinement of the output over a series of days. But the figure changes dramatically from image to image, all the way to the end of the run. At the very least it seems the output is not stable: a human has to decide when to stop the Wheel of Fortune. It looks more like a series of images taken from different training sets…

> They purport to show the progressive refinement of the output over a series of days. But the figure changes dramatically from image to image, all the way to the end of the run.

What they are probably doing is showing snapshots of the same noise vector (==random seed) for various epoches. Since the mapping of noise vector ~> face is totally arbitrary, the ProGAN is free to vary it as it pleases; thus, some but not perfect stability. I saw the same thing in messing around with anime GANs: a fixed set of noise vectors would show the anime faces change eye or hair color etc.

> At the very least it seems the output is not stable: a human has to decide when to stop the Wheel of Fortune.

Yeah, you can't do principled early stopping with GANs, really, because there's no held-out set and the loss is changing. I always ran until it diverged or I became impatient, and similarly with ProGAN: they ran as long as they could (takes like a week on big GPUs). To some extent, if you're using Wasserstein losses, the discriminator loss is supposed to be meaningful as a kind of absolute distance between the true image distribution and the generator distribution so you can do early stopping like 'stop if no improvement for 3 epochs'. (This is just in the pure generative approach; if you're using GANs for a semi-supervised application, presumably you can do early stopping as usual based on whatever you have held-out.)

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#72

Post by vinn124 »

claytonjy wrote: post #67
visarga wrote: post #21

Earlier quoted context omitted.

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 co…

> 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.

that is true: popular rl techniques (eg policy gradients) are very similar to "vanilla" supervised learning techniques and architectures, but they are unsupervised in the sense that they required zero human input.

alphago zero is the canonical example of tabula rasa machine learning.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#73

Post by bitL »

claytonjy wrote: post #67
visarga wrote: post #21

Earlier quoted context omitted.

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 co…

> 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.

RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple. It requires huge amount of tries/episodes and properly tuned exploration-exploitation ratio. It is somewhat based on biological reinforcement learning, so there might be basis in reality as it is with convolutional neural networks and visual field maps in visual cortex (even if very rough approximation). DRL is the technique that allows modeling decisions; so for predictions you have CNN/RNN/FCN, for generation GANs and for decisions DRL; together they are closest to AGI we have right now.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#74

Post by sago »

pk78 wrote: post #57
sago wrote: post #38

I don't understand the images associated with that article. They purport to show the progressive refinement of the output over a series of days. But the figure changes dramatically from image to image, all the way to the end of the run. At the very least it seems the output is not stable: a human has to decide when to stop the Wheel of Fortune. It looks more like a series of images taken from different training sets…

I am not sure if "unstable" is the word I would use. Sure, even after training for days the GAN produces not-so-realistic images, but the rate at which it generates those images gradually decreases over the training period and the images get more "realistic". >How do you solve the 'where to stop' problem if the output is so unstable? Looking at the discriminator loss would be a good start for that.

It's not the quality I was referring to. Look at the main image sequence. The images from 0 to, say, Day 5 show the kind of progressive refinement I expected: the network is improving its image over time. Each image is a refinement of the previous.

But compare the images from Day 5 the end. Eye colour is changing and then changing back. As is the background. And the hair colour. The position of the parting. Whether the mouth is closed or showing teeth. Day 16 is not an intermediate point between Day 9 and Day 18.

If it runs for another couple of days, would we get another version like Day 16?

That's what I mean by instability.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#75

Post by halflings »

bitL wrote: post #73
claytonjy wrote: post #67

Earlier quoted context omitted.

> 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.

RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple. It requires huge amount of tries/episodes and properly tuned exploration-exploitation ratio. It is somewhat based on biological reinforcement learning, so there might be basis in reality as it is with convolutional neural networks and visual field maps in visual cortex (even if very rough approximation). DRL is th…

> RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple.

There's plenty of RL papers using RNNs and some types of memory networks.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#76

Post by unsined »

oillio wrote: post #12

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/8e...

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.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#77

Post by bitL »

halflings wrote: post #75
bitL wrote: post #73

Earlier quoted context omitted.

RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple. It requires huge amount of tries/episodes and properly tuned exploration-exploitation ratio. It is somewhat based on biological reinforcement learning, so there might be basis in reality as it is with convolutional neural networks and visual field maps in visual cortex (even if very rough approximation). DRL is th…

> RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple. There's plenty of RL papers using RNNs and some types of memory networks.

Likely as value function approximators for one piece of the whole algorithm (as is the case with DQN/DDQN). However the main algorithm is likely using variation of Bellman equation, that assumes Markovian property and gives strong guarantees about convergence.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#78

Post by gwern »

bitL wrote: post #77
halflings wrote: post #75

Earlier quoted context omitted.

> RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple. There's plenty of RL papers using RNNs and some types of memory networks.

Likely as value function approximators for one piece of the whole algorithm (as is the case with DQN/DDQN). However the main algorithm is likely using variation of Bellman equation, that assumes Markovian property and gives strong guarantees about convergence.

If you're using DQN or pretty much anything in DRL, you don't have any guarantees about convergence in the first place, and using a RNN does give you the history summary you need (at least up to the minimum error achievable with that fixed-length summary, not that that is any more likely to converge than the overall DRL algo is).

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#79

Post by Retric »

delecti wrote: post #63
Retric wrote: post #62

Earlier quoted context omitted.

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.

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.

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.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#80

Post by pk78 »

sago wrote: post #74
pk78 wrote: post #57

Earlier quoted context omitted.

I am not sure if "unstable" is the word I would use. Sure, even after training for days the GAN produces not-so-realistic images, but the rate at which it generates those images gradually decreases over the training period and the images get more "realistic". >How do you solve the 'where to stop' problem if the output is so unstable? Looking at the discriminator loss would be a good start for that.

It's not the quality I was referring to. Look at the main image sequence. The images from 0 to, say, Day 5 show the kind of progressive refinement I expected: the network is improving its image over time. Each image is a refinement of the previous. But compare the images from Day 5 the end. Eye colour is changing and then changing back. As is the background. And the hair colour. The position of the parting. Whether t…

Ah, I understand what you are saying. The instabilities could be explained by the batches sampled during those training days and the generator's input. Training a GAN is not very straightforward and even minor changes in batch sampling could produce vastly different generated images.
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