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Subpixel: A subpixel convolutional neural network implementation with Tensorflow

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Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#31

Post by Keyframe »

trobertson wrote: post #30
Keyframe wrote: post #28

This is impressive! But, I'll be really impressed once this 'new thing' brings us roto masks in motion. That is, isolating objects from background on a movie with pixel-perfect accuracy. It will also make a lot of people out of job and a lot of people happy at the same time.

Considering motion blur, "pixel-perfect" is a difficult requirement.

There are ways around it. In case of motion blur, edge has a 'feather' where mask's alpha is a gradient. In severe cases, mask's curve has a lot of control segments with each having a different in and out feather defined.

edit:

example: https://youtu.be/yZyIYUEfT3U?t=71

Also, masks themselves can be motion blurred, and if motion blur approximation is close enough to the footage, then it's good https://www.youtube.com/watch?v=biginQL6NIo

And, what it looks like pulling a matte with state-of-the-art tools https://www.youtube.com/watch?v=8oQqr6Lfmag Still a pain.

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#32

Post by nullc »

Eliezer wrote: post #12
maxander wrote: post #2

So, basically, this is the thing in a crime detective movie where the forensic analyst is looking at a terrible pixelated surveillance camera still and says "enhance," and the computer magically increases the resolution to reveal the culprit's face. Just another entry on the "things that are supposed to be impossible that convolutional nets can do now."

And that's how that guy whose face appeared a few times in ImageNet became the world's most wanted terrorist, on the run for thousands of crimes.

Ding ding.

But good luck convincing a jury that a maximum likelihood decode from a few grainy pixels wasn't reliable when it gave a crystal clear output.

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#33

Post by [deleted] »

Roboprog wrote: post #4

Interesting image "upscale" algorithm. I'm not familiar enough with the field to understand how the "neutral net" part feeds in, other than to do parallel computation on the x-pos, y-pos, (RGB) color-type-intensity tensor interpolated/weighted into a larger/finer tensor. (linear algebra speak for upscaling my old DVD to HD, that sort of thing) At the risk of exposing my ignorance, this has nothing to do with "AI", ri…

[deleted]

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#34

Post by utkarshsinha »

eejr wrote: post #6
Roboprog wrote: post #4

Interesting image "upscale" algorithm. I'm not familiar enough with the field to understand how the "neutral net" part feeds in, other than to do parallel computation on the x-pos, y-pos, (RGB) color-type-intensity tensor interpolated/weighted into a larger/finer tensor. (linear algebra speak for upscaling my old DVD to HD, that sort of thing) At the risk of exposing my ignorance, this has nothing to do with "AI", ri…

yeah, no AI. Its low level computer vision. There is no implicit understanding of the scene to enhance it here. We show the neural nets several examples of low and high quality images it learns a function that makes the low quality looks more like the high quality. this may make you feel disappointed now, but in the write up we are also pitching this same module to be used in generative networks and other models that…

Wait, the neural network encodes within itself probability distributions of the various image patches it has seen. This is sort of like AI.

Approaches in the past used heuristics (like finding edges and upsampling them, etc). Those were fragile systems. In this approach, the system learns what's appropriate on its own.

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#35

Post by CamperBob2 »

paulsutter wrote: post #7
eejr wrote: post #3

Earlier quoted context omitted.

yup, to certain point! there are information theoretic limits though. You can fill in information, but there will be biases to a certain point. in this case defined by the dataset. if the "enhance" is too strong, we should be careful with what we do with the results in forensics. but man, it can make your internet pics look smooth! :) thanks for the comment!

If you have multiple images of the same scene (for example, from video frames), you should be able to use information across frames for a true enhancement?

Yes, and it's almost ridiculous how well that can work:

https://www.youtube.com/watch?v=ONZcjs1Pjmk

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#36

Post by andrewprock »

utkarshsinha wrote: post #34
eejr wrote: post #6

Earlier quoted context omitted.

yeah, no AI. Its low level computer vision. There is no implicit understanding of the scene to enhance it here. We show the neural nets several examples of low and high quality images it learns a function that makes the low quality looks more like the high quality. this may make you feel disappointed now, but in the write up we are also pitching this same module to be used in generative networks and other models that…

Wait, the neural network encodes within itself probability distributions of the various image patches it has seen. This is sort of like AI. Approaches in the past used heuristics (like finding edges and upsampling them, etc). Those were fragile systems. In this approach, the system learns what's appropriate on its own.

This is not AI in any real sense. It is a fairly straightforward machine learning application to computer vision.

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#37

Post by andrewprock »

tree_of_item wrote: post #29
Roboprog wrote: post #4

Interesting image "upscale" algorithm. I'm not familiar enough with the field to understand how the "neutral net" part feeds in, other than to do parallel computation on the x-pos, y-pos, (RGB) color-type-intensity tensor interpolated/weighted into a larger/finer tensor. (linear algebra speak for upscaling my old DVD to HD, that sort of thing) At the risk of exposing my ignorance, this has nothing to do with "AI", ri…

Everything that we understand how to do is "not really AI". It's only "AI" when it's still a mystery. At least that's the way people act.

I would expect AI to include some sort "emergent behavior", so in a sense you are correct. If a program does exactly what we expect it to, exactly how we tell it to, it almost certainly isn't AI.

Unless we are telling it to "be intelligent" whatever that means.

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#39

Post by nerdponx »

nullc wrote: post #32
Eliezer wrote: post #12

Earlier quoted context omitted.

And that's how that guy whose face appeared a few times in ImageNet became the world's most wanted terrorist, on the run for thousands of crimes.

Ding ding. But good luck convincing a jury that a maximum likelihood decode from a few grainy pixels wasn't reliable when it gave a crystal clear output.

http://www.slate.com/articles/news_and_politics/jurisprudenc...

Re: Subpixel: A subpixel convolutional neural network implementation with Tensorflow

#40

Post by witty_username »

andrewprock wrote: post #37
tree_of_item wrote: post #29

Earlier quoted context omitted.

Everything that we understand how to do is "not really AI". It's only "AI" when it's still a mystery. At least that's the way people act.

I would expect AI to include some sort "emergent behavior", so in a sense you are correct. If a program does exactly what we expect it to, exactly how we tell it to, it almost certainly isn't AI. Unless we are telling it to "be intelligent" whatever that means.

> As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities currently classified as AI include successfully understanding human speech,[4] competing at a high level in strategic game systems (such as Chess and Go[5]), self-driving cars, and interpreting complex data.

(from https://en.wikipedia.org/wiki/Artificial_intelligence)

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