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Image-to-Image Translation with Conditional Adversarial Nets

phillipi.github.io

51–60 of 61 posts

Re: Image-to-Image Translation with Conditional Adversarial Nets

#51
post #3

This is awesome! Makes me wonder how this can apply to image and video compression. You could send over the semantic segmentation version of an image or video, and system on the other end would use these technique to reconstruct the original.

I understood that to be the tech behind "Silicon Valley".

Re: Image-to-Image Translation with Conditional Adversarial Nets

#52
post #4

The "sketches to handbags" example, which is buried toward the bottom, is really cool. It's basically an extension of the "edges to handbags," but with hand-drawn sketches. Even though the sketches are fairly crude, with no shading and a low level of detail, many of the generated images look like they could, in fact, be real handbags. They still have the mark of a generated image (e.g. weird mottling) but they're tot…

There was a paper at CVPR 2016 called "Sketch Me That Shoe," which basically converted hand sketches to images using tied embedding networks. https://www.eecs.qmul.ac.uk/~qian/Project_cvpr16.html

Re: Image-to-Image Translation with Conditional Adversarial Nets

#53

Does anyone else have the feeling that with the current trajectory, something exactly like this, but with perhaps a million times the amount of feedback and data, thought will just emerge ? Yes, this is all 2D and abstract/selective training sets etc, but what if AI is the ultimate fake-it-until-you-make-it?

Didn't it just emerge with humans? I don't see why it couldn't happen again. There may be a specific structure or wiring that facilitates thought, but i suspect any large enough net with enough training data can do it.

Re: Image-to-Image Translation with Conditional Adversarial Nets

#54
post #28

Earlier quoted context omitted.

"Sketch your perfect handbag" may be a bit much to ask of most people.

Draw your perfect handbag to share with friends. you only need 10 buyers to have it created.

I doubt friends want the same handbag through! It's an anti-viral feature: Personalization.

Re: Image-to-Image Translation with Conditional Adversarial Nets

#56
post #3

This is awesome! Makes me wonder how this can apply to image and video compression. You could send over the semantic segmentation version of an image or video, and system on the other end would use these technique to reconstruct the original.

https://research.googleblog.com/2016/09/image-compression-wi...

Re: Image-to-Image Translation with Conditional Adversarial Nets

#57
post #37
post #35

Earlier quoted context omitted.

Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. Randomness is injected into all brain processes on account that biological neurons are stochastic. So there is an amount of randomness mixed into eve…

> Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. On what basis do you make this claim? Humans are empirically terrible random number generators. If you ask someone to pick a random number, the res…

Do you think humans are good at anything or just generally useless ?

Re: Image-to-Image Translation with Conditional Adversarial Nets

#58
post #29

Earlier quoted context omitted.

I feel like we would end up here: http://www.gianlucagimini.it/prototypes/velocipedia.html

I wonder if you were to average the design of the bicycles whether it would actually produce something that works?

I would have thought that, if you are smart enough to find a "bicycle vector space" in which averaging sketches of a bicycle produces another valid sketch of a bicycle, then you probably already know enough about bicycles to design one without the input of imperfect sketches.

Re: Image-to-Image Translation with Conditional Adversarial Nets

#59
post #37

Earlier quoted context omitted.

> Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. On what basis do you make this claim? Humans are empirically terrible random number generators. If you ask someone to pick a random number, the res…

Do you think humans are good at anything or just generally useless ?

That's a really bizarre question. I think humans are good at a lot of things. I also think this is utterly irrelevant.

Re: Image-to-Image Translation with Conditional Adversarial Nets

#60
post #40
post #9

Earlier quoted context omitted.

> real creativity What is real creativity? Creativity is just random noise converted into patterns. Is the computer variety of creativity not real enough?

While I do not accept ragebol's notion of real creativity, I very much doubt your definition is correct. The use of ' just ' is too strong, excluding the possibility of influence from other inputs. When humans learn, they do so by learning a large set of arbitrary relationships which further trigger complex associations (this is blue, it is used for X, it is like Y, which is like Z). These relationships are further b…

Thanks. I approve of your observations and especially like this part:

> AGI more and more looks like it will be about striking the right balance between generation and selection.

I was thinking AGI is essentially reinforcement learning on top of rich, predictive models of the world. But you could see it as the balance between generation and selection.

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