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GAN Theft Auto [video]

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Re: GAN Theft Auto [video]

#61

Post by teruakohatu »

sentdex wrote: post #14
rasz wrote: post #12

Earlier quoted context omitted.

One of the main problems with ML/NN is it often works like magic, aka the trick works as long as audience doesnt know the secret behind it. Its fascinating to gullible audience, mundane bordering on boring to practitioners. My Tiger repelling rock^^^^^^leopard detection model works great on all animal pictures ... until you feed it a sofa https://web.archive.org/web/20150703094328/http://rocknrolln... >able to genera…

In the end, everything is boiling down to matrix math, so you can always make the argument that no neural network is impressive if you want. The model's size is ~173MB, depending on settings. That's not much space to have memorized every single possible combination of events, nor was our data enough to cover that either.

> The model's size is ~173MB

That is impressive! Less than twice the size of ResNet-50 weights. Surely that is within an order of magnitude of an equivalent Unity or GoDot game+models.

Re: GAN Theft Auto [video]

#62

Post by haecceity »

sentdex wrote: post #14
rasz wrote: post #12

Earlier quoted context omitted.

One of the main problems with ML/NN is it often works like magic, aka the trick works as long as audience doesnt know the secret behind it. Its fascinating to gullible audience, mundane bordering on boring to practitioners. My Tiger repelling rock^^^^^^leopard detection model works great on all animal pictures ... until you feed it a sofa https://web.archive.org/web/20150703094328/http://rocknrolln... >able to genera…

In the end, everything is boiling down to matrix math, so you can always make the argument that no neural network is impressive if you want. The model's size is ~173MB, depending on settings. That's not much space to have memorized every single possible combination of events, nor was our data enough to cover that either.

Video frame resolution is pretty small...

Re: GAN Theft Auto [video]

#63

Post by toxik »

sentdex wrote: post #42
emptyparadise wrote: post #25

One throwaway line about GAN operating systems now made me want to see a shell GAN. Keypresses as inputs, 80x24 terminal screens as outputs. Could a neural network dream of Unix?

I don't see why not. Might be something fun to try tbh.

I always imagined this for a honeypot, an SSH server that accepts any and all! root/password? You bet!

Re: GAN Theft Auto [video]

#64

Post by codetrotter »

sentdex wrote: post #17
ShamelessC wrote: post #10

Great work! Hacker News still seems to have a deeply skeptical culture with regard to machine learning - not sure why. There's always someone saying it's "not novel" and it's "just doing x". Overfitting is a known issues in machine learning, people. If you still think all neural networks are doing is memorizing the dataset completely in the year 2021 - you might want to revisit the topic. It is one of the first conce…

Heh, yeah, tough crowd I guess. The full code, models, and videos are all released and people are still skeptical. I feel like 95%+ of papers don't do anything besides tell you what happened and you're just supposed to believe them. Drives me nuts. Not sure why all the hate when you could just see for yourself. I'd welcome someone who can actually prove the model just "memorized" every combo possible and didn't do an…

FWIW I saw your video a couple of days ago via Reddit and I loved it a lot. Even sent a link to the video to a friend of mine because I think it was a very inspiring and interesting video.

I hope you don't let naysayers get to you :)

Re: GAN Theft Auto [video]

#65

Post by Sharlin »

whalesalad wrote: post #27

GPUs: am I a joke to you? Instead of using them to render polygons, let’s use them to train neural networks that produce models that make them unnecessary. I’m oversimplifying - but pretty wild nonetheless.

Wait, you mean that "GPU" doesn't mean "GAN Processing Unit"? ;)

Re: GAN Theft Auto [video]

#66

Post by justinjlynn »

danuker wrote: post #53
godelski wrote: post #31

Earlier quoted context omitted.

> I feel like 95%+ of papers don't do anything besides tell you what happened and you're just supposed to believe them. Honestly I think there's a big problem with page limits. My team recently had a pre-print that was well over 10 pages and we still didn't get everything and then when we submitted to NeurlIPS we had to reduce it to 9! This seems to be a common problem and why you should often check different version…

There's the Hutter Prize [1] - memorizing is useful (and arguably intelligent) if it's compressed. http://prize.hutter1.net/

Indeed. Novel, efficient program synthesis is still novel, efficient program synthesis even if it's a novel, efficient data compression codec you're synthesising.

Re: GAN Theft Auto [video]

#67

Post by okamiueru »

tiborsaas wrote: post #60
okamiueru wrote: post #59

Can someone explain a bit more on the long term applicability, or maybe other use cases that might be easier to appreciate? The reason why I ask is that it seems very challenging to generate the training data for such systems. Could someone explain how this can go further than to just replicating X? So, if assuming some creative freedom, could you give an idea of what the long term application of this would be? NB: p…

In the future we might have a fourth common media format besides pictures, videos and audio: GAN records.

That is an interesting thought. I don't fully understand how though. The main challenge is the training data. If you need to first create the interactive experience... What would the added value be?

Re: GAN Theft Auto [video]

#68

Post by ludwigschubert »

nitrogen wrote: post #37

Something I'd like to see is a visualization of subsets of the network's internal state that correlate with simple quantities like compass direction, velocity, position, etc. It'd be really fascinating to see where in the model these things are being learned, whether they are concentrated in a small area or spread out, and whether this is somewhat consistent across different iterations of the model.

Me too! In a much simpler setting a former colleague of mine, Jacob Hilton, tried such an exploration for the vision part of a OpenAI CoinRun model. It’s the first part of this paper: https://distill.pub/2020/understanding-rl-vision/

Re: GAN Theft Auto [video]

#69

Post by akiselev »

dkarras wrote: post #28
andrepd wrote: post #22

Earlier quoted context omitted.

> Hacker News still seems to have a deeply skeptical culture with regard to machine learning Is... that a bad thing? Skepticism is good. When it's about something as hyped as "deep learning", even more so.

>Is... that a bad thing? Yes, when it is there for no valid reason, or ridiculous reasons. Skepticism is not a default position you can take like a toddler refusing to eat their vegetables. You need some informed (and non-fallacious) intelligent reasoning behind that. "I'm skeptic about this thing using X because X is so hyped these days" is not such reasoning.

"I'm skeptical about this thing using X to do Y because the burden of proof is on people claiming X does Y and historically they have failed to meet that burden"

I don't know what skepticism has to do with ridiculous toddlers - they are almost universally incapable of grasping the nuances of epistemology.

Re: GAN Theft Auto [video]

#70

Post by TinkersW »

Looks interesting, if very far from practical-- too bad it requires a "DGX" station to train

It seems to flicker/fade things in alot, like the random poles that keep appearing and disappearing, it seems like there is not enough focus on temporal consistency or something?

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