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Deep image prior 'learns' on just one image

dmitryulyanov.github.io

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Re: Deep image prior 'learns' on just one image

#211

Post by ac2u »

wonderous wrote: post #180
ac2u wrote: post #174

Earlier quoted context omitted.

No not really, this isn't generation for the sake of classification. It's generation in order to generate a dataset to train a new network that exhibits the characteristics of the network in the paper that can be used quickly and in a more general fashion.

Meaning that your assumption is that there is an optimal generalized network structure for generate a neural network as described in the paper and that as such, what is missing from the research is how to create an optimal way to generate a malformed source/target seed. Is that correct?

I'm not suggesting it's missing from the research as I don't have anywhere near the area of expertise to make that call.

But you're correct in your summary: I'm wondering if that's a possible follow up.

Re: Deep image prior 'learns' on just one image

#212

Post by jacquesm »

eanzenberg wrote: post #183
jacquesm wrote: post #142

Earlier quoted context omitted.

Your comment is hilariously wrong. Please do not make assumptions like this, you're typically going to be embarrassed.

Please show me a child that recognizes a new object after one look.

Shall I mail them to you?

Re: Deep image prior 'learns' on just one image

#213

Post by magoghm »

eximius wrote: post #50

Huh. This seems to boil down to 'noise is higher information entropy than realistic content; partial learning will learn realistic content before learning noise' or something like that.

Interesting. Then you might achieve similar results by compressing the image as a JPEG with low quality settings.

Re: Deep image prior 'learns' on just one image

#214

Post by rasz »

raz32dust wrote: post #25

This is incredible! Can't help but wonder if the brain does something similar to fill up "gaps" in reality. e.g how we fill up our perception (not only vision, but general mental intuition) based on just context.

Not exactly. Brain has prior knowledge (deep learning). Lets take a picture of a car as an example. License plate has letters/numbers, so even if it cuts lower half you will try to guess by fitting the most likely symbol combinations. Approach from the article has no prior knowledge, wouldnt be able to inpaint meaningful letters, nor guess most cars have wheels, bumpers, drive on the road etc.

Re: Deep image prior 'learns' on just one image

#215

Post by bitL »

scotty79 wrote: post #202
bitL wrote: post #177

Earlier quoted context omitted.

I think it's both C and NN. Don't forget each new C layer groups information from previous layers; using just a single C layer won't do you much good. It might not reflect brain much but it kinda resembles what retina/visual cortex neurons do; CNNs were actually inspired by visual field maps found in visual cortex and somebody had the idea that C is the most similar CV operation we have, and put them together. To eve…

Next layers have exactly same trick as the first one. I don't quite buy that it resembles visual cortex.

It's probably just very rough "resemblance" :D It is said CNNs were "inspired" by visual field maps; I am 100% sure we know very little about how that part of brain works and maybe somebody just took a look at main/thickest connections between neurons there and tried to assemble them in a NN to see if it helps.

Re: Deep image prior 'learns' on just one image

#217

Post by dahart »

nl wrote: post #72
sarreph wrote: post #15

Earlier quoted context omitted.

Actually... How cool would it be to have an NN that could extend an image's background with plausible scenery? Not just photoshop 'smart' fill, but for example if it detected a building on the right side of an image, it could draw the rest of it? :)

This exists. This approach takes other photos of the same scene to extend a cropped pic (see also MS PhotoSynth): http://grail.cs.washington.edu/projects/sq_photo_uncrop/ This uses a GAN to fill in missing parts of a pic. Those parts could be on the edge of the picture (although the paper doesn't explore that): http://hi.cs.waseda.ac.jp/~iizuka/projects/completion/data/c...

Here's another earlier example of un-crop, that uses only the source image (doesn't need gps or an internet database of photos), and does both out-painting and in-painting.

http://graphics.cs.cmu.edu/people/efros/research/EfrosLeung....

Re: Deep image prior 'learns' on just one image

#218

Post by scotty79 »

bitL wrote: post #215
scotty79 wrote: post #202

Earlier quoted context omitted.

Next layers have exactly same trick as the first one. I don't quite buy that it resembles visual cortex.

It's probably just very rough "resemblance" :D It is said CNNs were "inspired" by visual field maps; I am 100% sure we know very little about how that part of brain works and maybe somebody just took a look at main/thickest connections between neurons there and tried to assemble them in a NN to see if it helps.

I'm thinking exactly same thing.

Re: Deep image prior 'learns' on just one image

#219

Post by rdlecler1 »

vbarrielle wrote: post #146
rullelito wrote: post #138

Earlier quoted context omitted.

Can someone explain this in a way so that an ordinary mortal computer scientist can understand it?

> Can someone explain this in a way so that an ordinary mortal computer scientist can understand it? I'll try. Instead of the common approach that tries to search for image pixels to minimize e.g. a denoising objective, they realize that they can instead search for the weights of an image generator network such that the generated image matches the objective. They argue that the structure of the network then constitut…

What do they mean by ‘the structure of the network’ is this the network topology?

Re: Deep image prior 'learns' on just one image

#220

Post by Dylan16807 »

mjburgess wrote: post #168
yorwba wrote: post #158

Earlier quoted context omitted.

> Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do. Your remark made me research this, and apparently transparent gold exists, we can create it, and you can buy it [1]. The trick appears to be making the gold thin enough. > Line up everything in the known universe that is conscious and you will find that…

Sure, I meant we cannot impart the property of transparency to a gold bar by "programming" it. Programming isnt a magical spell that can rewrite the causal interactions that take place in the universe. > and you'll find a whole lot of things that most people wouldn't call "conscious" I disagree. I cannot think of anything with a nervous system engaging in the particular neurochemical reaction I'm talking about not be…

> Or to put it another way, a program which "precisely emulates the behavior of gold" doesnt turn machine into gold.

> A program which "precisely emulates the behavior of digestion" does not digest pizza.

> A program which "precisely emulates the behavior of" consciousness isnt conscious.

If my goal is probing the behavior of gold, I don't care if it's real or emulated.

If my goal is having a list of chemicals pizza turns into, I don't care if the digestion is real or emulated.

If my goal is having a conversation, I don't care if they're a p-zombie.

Tell me if I'm missing anything here:

Either a computer can perfectly imitate a neuron's behavior, or it can't.

If it can, then a giant computer should be able to make either a conscious being or a P-zombie. (Do you make a distinction there? If you do, can you justify it?)

If you say it can't, then I accuse you of magical thinking. There is no evidence of physical interactions that cannot be emulated.

> To speak as if the mind could be abstracted enough to a description that may be realized in silicon is to believe in an almost magical power of electric.

Here's the thing. The idea of emulating a person does not depend on abstracting the mind. The 'proof of concept' is just building a computer so big that you emulate an entire nervous system, fully intact, every single atom.

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