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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

#161

This shouldn't really be surprising. Machine learning is specifically not magic. The reason CNNs have seen so much success is precisely because they build in translation-invariance, which massively cuts down on parameters while forcing the final function to have the desired structure regardless of wherever gradient descent takes the weights. Also why most papers in deep learning are network architecture innovation.

Deep learning researchers rediscover Compressed Sensing. News at 11.

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

#162

Can someone break this down for this layman?

Not an expert so take this with a grain of salt; I could be misinterpreting the paper. It seems that the current accepted method is to train a network with distorted images as the input and the correct undistorted images as the targets. Then after training you can feed a new distorted image into the trained network and get the estimated "fixed" image. However this team actually uses the distorted image as both the in…

> for some reason, the structure of the network means that the estimated output learns realistic features first, and then overfits to the noise afterwords.

Why does this happen? What characteristics does the network structure have that cause this effect?

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

#163
post #21

Wow: "In this work, we show that, contrary to expectations, a great deal of image statistics are captured by the structure of a convolutional image generator rather than by any learned capability. This is particularly true for the statistics required to solve various image restoration problems, where the image prior is required to integrate information lost in the degradation processes. To show this, we apply untrain…

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

There's a great explanation of the idea here in the comments : https://news.ycombinator.com/item?id=15820994

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

#164
post #56

Earlier quoted context omitted.

Not an expert so take this with a grain of salt; I could be misinterpreting the paper. It seems that the current accepted method is to train a network with distorted images as the input and the correct undistorted images as the targets. Then after training you can feed a new distorted image into the trained network and get the estimated "fixed" image. However this team actually uses the distorted image as both the in…

This is fascinating because I've been running into something similar with sequence to sequence models translating natural language into Python code. I got better results stopping "early" when the perplexity was still quite high, I thought it was a little crazy.

Fascinating! Could you share more on the details of your experiment?

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

#165

Earlier quoted context omitted.

Where do you think human consciousness comes from?

People are conscious (actually self-aware?) because we say we are. Which isn't actually evidence. I'm doubtful there's anything to explain.

Lots of other animals are self-aware (they can identify themselves for example). This isn't just a human thing, but an advanced brain thing.

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

#166
post #21

Wow: "In this work, we show that, contrary to expectations, a great deal of image statistics are captured by the structure of a convolutional image generator rather than by any learned capability. This is particularly true for the statistics required to solve various image restoration problems, where the image prior is required to integrate information lost in the degradation processes. To show this, we apply untrain…

This is great work. It shows that some of the "amazing" results of deep learning are not as deep and don't even require learning! In my view this also sheds some light on the GAN's and their ability to generate "real looking images". Perhaps there is much less to generating "real looking images" then everyone attributes. E.g. in this work, the network clearly knows exactly nothing about the world and generates good l…

"the network clearly knows exactly nothing about the world"

Well, except the network structure itself.

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

#167

Earlier quoted context omitted.

Where do you think human consciousness comes from?

I think all consciousness (a feature of most animals) is a specific kind of neuro-bio-chemical process. To put it another way, to improve your focus I can give you Modafinil. To stabilize your mood lithium. To give you hallucinations, lsd. To distort your visual perception, ketamine. To make you more empathetic, trusting and less hostile: mdma. To speed up your thinking, cocaine. To make you aggressive, too much coca…

> An certainly not a soul

OK we agree on the basics - that consciousness is not due to some mystical external factor but that it is contained within a brain.

However, your description of consciousness is a particular combination of electrical fields (NB that those chemicals are acting by changing how electricity is transmitted)...

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

#168
post #158

Earlier quoted context omitted.

To throw a dissenting voice into the mix: I do not think intelligence is a consequence of structure. 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. Programming is the application of an electric field across a silicon surface: this can no more transmute silicon into gold as it can into a nervous system…

> 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 being conscious. No example comes to mind?

I dont mean any old reaction. As, the digestion of wheat. I mean the specific kind that define neurological systems.

> why do the details of the biochemical activity matter beyond being a particular physical realization of some differential equations

Because the "details" we're talking about are the causal effects.

> precisely emulates the behavior of that biochemical activity

For the same reason you cannot programme a gold bar into transparency. Or program a table into an elephant.

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.

By "emulation" you mean, "imitation in form". Since it is only the FORMS the program and the nervous system share (or, gold, whatever). Ie., that they can be both described to an extreme level of abstraction in the same way. But the universe isnt abstract. It isnt a form.

A program simulating a gold bar may be an instance of some equation that a gold bar is -- but possesses none of the properties that make it gold.

A machine which imitates some highly abstract equational description of thought is as close to thinking as a bird is to an aeroplane. The bird's heart will burn as much jet fuel as your machine will think.

The universe isnt taking place in the abstract, it's taking place in the concrete. You have no soul. Your mind is not a program. You consciousness is not ideal. It isnt a number. A pattern, a structure, an equation. These are descriptions. Your mind is something your body is doing -- as so it is for every known thing that has a mind.

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.

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

#170
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…

>> 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 being conscious. No example comes to mind?

Is a jellyfish conscious? Does it have the particular neurochemical reaction you are talking about?

> A machine which imitates some highly abstract equational description of thought is as close to thinking as a bird is to an aeroplane. The bird's heart will burn as much jet fuel as your machine will think.

This is actually a good analogy for our disagreement. Your definition of "thought" seems to inherently depend on the implementing substrate; and if it doesn't burn jet fuel, a bird doesn't really "fly".

But for me the substrate is irrelevant; I don't care whether a machine "really thinks", so long as it can solve any problem which I might have to "think" about otherwise.

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