Image-to-Image Translation with Conditional Adversarial Nets • Hacker News Board

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

#11

Post by visarga » Tue, Nov 22, 2016, 2:51 PM UTC

amelius wrote: post #2

I wonder how well this scales to a larger domain of interest. So, e.g., if the neural net needs to know not only about cars and nature, but about more topics such as people, faces, computers, gastronomy, santa claus, halloween, etcetera, how does the neural net scale? And how should its topology be extended under such scaling?

It's being researched with great interest. Building models from text and images, describing internal structure and relations between objects, building rich prior knowledge about the world in order to do inference and guide behavior.

I see lots of papers that go in this direction, of creating a rich, semantic, predictive representation of images, video and text and then using it as the basis for reinforcement learning. Learning to understand the world and to act based on that understanding.

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

#12

Post by drcode » Tue, Nov 22, 2016, 2:54 PM UTC

ragebol wrote: post #6

Interesting. What I like about the "Day to Night" example is that is clearly demonstrates that these sort of networks lack common sense. It expects light to be where they are clearly (to humans with common sense at least) no things that can produce light. E.g. in the middle of a roof or in a tree. Of course, there can be, but it's fairly uncommon. And the opposite as well, no lights where a human would totally expect…

I'd guess the problem is that the daytime pictures allow for easy feature detection (tree, building etc) but the nightime pictures are washed out- We humans look at the daytime picture first, then say "that nighttime picture must have a tree there" which involves feature detection across both pictures (in the training phase)

I suspect a neural network better specialized for this task (i.e. that has the data interlaced for both day and nighttime during training) would have no problem feature detecting trees and leaving them unlit.

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

#13

Post by willcodeforfoo » Tue, Nov 22, 2016, 2:55 PM UTC

The Aerial-to-Map example looks like this may be useful for automatic map/satellite rectification/georeferencing, but not sure how efficient it'd be if it has to compare against a large area.

Does anyone have any experience in this area?

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

#14

Post by espadrine » Tue, Nov 22, 2016, 2:57 PM UTC

sebleon wrote: 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.

You can perform extremely good compression this way, but the computational and energy cost would be prohibitive.

There are even more traditional tricks that don't make it in things like H.265 because it is too costly.

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

#15

Post by intoverflow2 » Tue, Nov 22, 2016, 3:01 PM UTC

visarga wrote: post #9

ragebol wrote: post #7

Earlier quoted context omitted.

It has the potential to redefine what we think of as 'creativity', as happened with what we consider intelligence and what we think of as "AI Hard" problems. Perhaps what these networks are generating can be labeled better as "Guided/constrained imitation" rather than real creativity.

> 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?

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

#16

Post by ape4 » Tue, Nov 22, 2016, 3:21 PM UTC

jawns wrote: 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.

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

#17

Post by verytrivial » Tue, Nov 22, 2016, 3:23 PM UTC

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?

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

#18

Post by [deleted] » Tue, Nov 22, 2016, 3:33 PM UTC

jawns wrote: 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…

[deleted]

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

#19

Post by mshenfield » Tue, Nov 22, 2016, 3:34 PM UTC

Just want to throw out that none of these applications are new. What is novel about their approach is that, instead of learning a mapping function using a hand-picked function to quantify accuracy for each problem, they also have a mechanism for choosing the function that quantifies accuracy. Haven't grokked the paper to see how they do it, but that is pretty neat IMO.

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

#20

Post by TPCrow » Tue, Nov 22, 2016, 3:37 PM UTC

dpark wrote: post #10

visarga wrote: 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?

> Creativity is just random noise converted into patterns. This is not a consensus definition. Creativity doesn't actually seem to be very random at all according to the people who study it.

Creativity isn't magic.

Humans are not magically creative as much as they'd like to be.

If i ask you to think of a random number, you don't just pull it out of thin air, It can be based on tens to hundres of things: -Should i do a relaly low or high number? -People always use round numbers that end in 0 or 5, maybe i shouldn't do that, or should i to make it seem truer -what other large "random" numbers have a heard? -i remember seeing a number recently, maybe try a modification of that -you used {x} as a random number last time, go similar to that?

All this adds up in that under a second thought you have when i asked you to think of a random number. the literal same thing goes into all creative works, the output is a function of the input.