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PixelNN – Example-Based Image Synthesis

cs.cmu.edu

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Re: PixelNN – Example-Based Image Synthesis

#81

Post by TuringTest »

stepik777 wrote: post #72
maho wrote: post #21

I hope some day this will generalize to video. I don't care about the exact shape of background trees in an action movie - with this approach, they could be compressed to just a few bytes, regardless of resolution.

Except that it can put trees somewhere where there were no trees but something similar to them. Or it can put face of a more popular actor instead of an actual less popular one because it was more often present in the training dataset. No, thanks.

Well, isn't that basically how Hollywood makes blockbusters?

Re: PixelNN – Example-Based Image Synthesis

#82

Post by LeifCarrotson »

sweezyjeezy wrote: post #42
ACow_Adonis wrote: post #4

Earlier quoted context omitted.

Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation". You fundamentally can't get back information that has been destroyed/or never captured in the first place. What you can do is fill in the gaps/information with plausible values. I don't know whether this sounds like I'm splitting hairs, but it's really important that the general publ…

> Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation". I would argue that this is a form of enhancement though, and in some cases will be enough to completely reconstruct the original image. For example, if I give you a scanned PDF, and you know for a fact that it was size 12 black Ariel text on a white background, this can feasibly le…

Or it might not! This reminds me of the Xerox bug from a couple years ago, that turned one number into another.

http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...

Enhancing some things incorrectly would be worse than leaving them ambiguous.

Re: PixelNN – Example-Based Image Synthesis

#83

Post by joosters »

cbr wrote: post #78
joosters wrote: post #75

I don't understand how the edges-to-faces can possibly work. The inputs seem to be black & white, and yet the output pictures have light skin tones. How can their algorithm work out the skin tone from a colourless image. Perhaps their training data only had white people in it?

Color comes from the initial neural network step. Since skin color is relatively predictable from facial features (ex: nose width), it should be able to do reasonably well.

Really? With what accuracy? This is the kind of assumption that will get research groups into very deep water...

Just imagine the kind of CCTV usage being discussed elsewhere in this thread. But the neural network happens to have a wrong bias towards skin colour...

Re: PixelNN – Example-Based Image Synthesis

#84

Post by jj12345 »

imaginenore wrote: post #63

It almost looks like they mixed training and testing data in some of the examples. The bottom-left sample in the normals-to-faces is extremely suspicions.

I was looking at this as well, but I'm willing to suspend my disbelief because the normal vaguely looks like it has a good deal of information (in a basic fidelity sense).

Re: PixelNN – Example-Based Image Synthesis

#85

Post by negamax »

avian wrote: post #25

I found the title somewhat misleading. I was expecting some clever application of the nearest-neighbor interpolation. But this seems to involve neural nets and appears far from "simple" to me (I'm not in the image processing field though).

I was impressed that title didn't say AI..

Re: PixelNN – Example-Based Image Synthesis

#86

Post by jokoon »

I have a large collection of images, many being accessible through google image search.

I wonder if there could be a way to "index" those images so I can find them back without storing the whole image, using some type of clever image histogram or hashing-kind function.

I wonder if that thing already exist, since there are many images, and since most images have a lot of difference in their data, could it be possible to create some kind of function that describe an image in a way that entering such histogram redirects to (or the closest) the image it indexed? I guess I'm lacking the math, but it sounds like some "averaging" hashing function.

Re: PixelNN – Example-Based Image Synthesis

#88

Post by danielmorozoff »

jokoon wrote: post #86

I have a large collection of images, many being accessible through google image search. I wonder if there could be a way to "index" those images so I can find them back without storing the whole image, using some type of clever image histogram or hashing-kind function. I wonder if that thing already exist, since there are many images, and since most images have a lot of difference in their data, could it be possible…

This is the current approach for large sale image retrieval. By using some model to extract features and then performing distance calculations. This is usually done with hashing once speed and the size of the dataset become large.

Re: PixelNN – Example-Based Image Synthesis

#90

Post by dannyw »

jokoon wrote: post #86

I have a large collection of images, many being accessible through google image search. I wonder if there could be a way to "index" those images so I can find them back without storing the whole image, using some type of clever image histogram or hashing-kind function. I wonder if that thing already exist, since there are many images, and since most images have a lot of difference in their data, could it be possible…

That's perceptual hashing. Check out https://www.phash.org/
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