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Image-to-Image Translation with Conditional Adversarial Nets

phillipi.github.io

21–30 of 61 posts

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

#21

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?

If you can define thought and it can be implemented, sure.

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

#23
post #16
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…

This is be a popular shopping website. Sketch your perfect handbag. See an image of the product. Click to buy.

[deleted]

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

#24
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?

Why are some people better at it than others then if it's purely noise and patterns?

They do a worse job converting the noise into patterns, relative to some common judge of how good the solution is. See any GAN in its early stages of training for an example.

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

#26
post #5

I feel this can potentially revolutionize creative processes, for example in the clothing industry. You just draw up a purse or a shoe, let the machines generate dozens of variants (with pictures), and then you only have to filter and rank them. You can pipe these product sketches directly into focus groups who tell you which product is most likely to sell. You don't need massive staff to come up with product variant…

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

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

#27

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?

I don't see this happening. What I do see happening, is it figuring us out. Somewhere out there, there's a function which explains how exactly our society is completely organized in every way.

From that, the AI could generate books, movies, and do a lot of things.

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

#28
post #16
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…

This is be a popular shopping website. Sketch your perfect handbag. See an image of the product. Click to buy.

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

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

#29
post #5

I feel this can potentially revolutionize creative processes, for example in the clothing industry. You just draw up a purse or a shoe, let the machines generate dozens of variants (with pictures), and then you only have to filter and rank them. You can pipe these product sketches directly into focus groups who tell you which product is most likely to sell. You don't need massive staff to come up with product variant…

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?

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

#30
post #28
post #16

Earlier quoted context omitted.

This is be a popular shopping website. Sketch your perfect handbag. See an image of the product. Click to buy.

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

It is, but this sort of approach is easily applied to transformations (in fact, an earlier GAN HN submission was "Generative Visual Manipulation on the Natural Image Manifold" https://people.eecs.berkeley.edu/~junyanz/projects/gvm/ , Zhu et al 2016b ). So you can start with a lousy sketch and transform it until it works, or you can start with a pre-populated set of handbags, recognize the one closest to what you want, and tweak/sketch that.
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