[deleted]
Deep image prior 'learns' on just one image
201–210 of 235 posts
Re: Deep image prior 'learns' on just one image
#202Earlier quoted context omitted.
It's not about neural network architecture. CNNs are taught by presenting them overlapping pieces of image. To speed things up and keep things orgsnized this is not done sequentially but in parralel making multiple neurons share weights but this is just a trick. So what makes this result possible is not the architecture of NN in CNN but rather architecture of C. That allows us to get multiple samples from single imag…
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.
Re: Deep image prior 'learns' on just one image
#203Earlier quoted context omitted.
This can also be looked at as the original source of patch based denoising, etc. In the end it's about capturing the scaling properties and self similarity of natural images. This is also, for example, why wavelets were so effective as a basis. David Mumford particularly did some great work on this sort of thing a couple of decades ago, along with many others. I hope when people are rushing around trying to apply con…
The benefit of CNNs is like the benefits of SVMs -- they generalize all the great old techniques so you don't have to understand them all, you just throw more CPU at the optimization problem.
I don't think that's true particularly in this case.
This paper is pointing out that you can encode a structural prior in a CNN - but knowing the "great old techniques" will help you design the right network architecture to do that.
SVMs were a surprise when they came out, no so much a generalization as a challenge (at least at first)
Re: Deep image prior 'learns' on just one image
#204Earlier quoted context omitted.
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…
> 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.
OK, well then your calculator satisfies your definition of "thinking".
I'm concerned to know whether a machine is doing what my dog is or I am. And mostly when people become hysterical or tedtalky (which is the same thing most of the time) about AI they are presenting an "I, Robot" future where androids dream of electric sheep.
> and if it doesn't burn jet fuel, a bird doesn't really "fly"
When I think, "I'd like my pen" and subsequently my arm moves to get my pen, my thinking is causally connected to my arm moving. My arm moving is some chemical my muscles do, in order to be connected at all with my thinking, my thinking has to be something broadly chemical too.
The plane doesn't move air out of the way because its flying. It does that because its burning fuel (etc.). "Flying" as a description of what the bird and the aeroplane are both doing isnt actually any physical process at all. Is a pattern they both very abstractly follow that we have invented. In this sense nothing in the universe actually flies: the bird does its thing, the aeroplane does its thing --- and from our point of view, they are both abstractly similar.
IT's our pov which makes them similar though. The airplane isnt distressed to burn too much. The bird is.
Re: Deep image prior 'learns' on just one image
#205Wow: "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…
I have been reading about fractal image compression (don't sue me patent trolls, I haven't implemented anything I promise!) lately and so this doesn't surprise me very much. I also wouldn't be surprised if it turned out that what is happening inside the network is essentially similar in many respects to how fractal image compression works.
Fractal image compression is inherently pretty simple in concept: Find portions of the image that are similar to transformed other portions of the image. Store the things needed to transform one portion into the other, and patch together enough of those to cover the whole thing. Then you can throw out the image itself. You only need the ways in which the parts can be transformed into the others (affine transformations along with brightness/contrast shifts usually). Once you have those, you can literally start with _any_ source image and iterating the application of the transformations are guaranteed to result in something very close to the original image.
A CNN 'rediscovering' this technique feels intuitively like a very natural thing to occur, and the iterative images presented there smack of early iterations of a fractal image 'decoding' from a blank source image. The connection, of course, could be utterly specious and I am just guessing. I am intrigued, however, as I've been wanting to investigate using deep learning to perform VHS video capture cleanup as a side project for awhile now.
Re: Deep image prior 'learns' on just one image
#206Wow: "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?
Here's a psuedocode rundown of exactly what it does:
network = new NeuralNetwork()
targetOutput = readFile('./images/zebra-500x500-corrupted.jpg')
input = generateNoise(500, 500)
while(iterationCount Re: Deep image prior 'learns' on just one image
#207Wow: "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…
> PS. This makes me wonder whether and to what degree the structure of the brain's connectome is a necessary prior for AGI. Well, I wouldn't mix up AGI and AGI by deep learning, and more important I would emphasise that this is a good prior for images . The fundamental insight in CNNs and eventually in this work is that there is a correlation between pairs of nearby pixels. We have something similar for video and aud…
Well, there is correlation between 'nearby' things of all types. An apple is an 'apple' because we encountered the word and the thing in close proximity to one another temporally and perceptively many times during our life. All knowledge is this. Tremendously overlapping and filled with mistakes (spurious correlations induced by coincidence and happenstance), but fundamentally just that same 'insight'. Our brains are association machines. Once sufficient quantity of associations exist in a brain in a body living in a world we recognize, we call the property that results 'consciousness'.
Unfortunately there is no reason to think that you can substract the 'in a body living in a world we recognize' part and end up with anything like the same property. All those associations come from perceptive inputs, and result in motive outputs, with the biofeedback that occurs being of profoundly fundamental importance (heck, put a pen in your mouth which causes your mouth to 'smile' and you will 'feel happier'). While of course there is definitely a possibility that a machine-based intelligence could be conscious, it would doubtless have to be very different from anything we could recognize, derived as it would be from its own input, output, and the feedback between the two.
Re: Deep image prior 'learns' on just one image
#208Earlier quoted context omitted.
> Of all the known things in the universe which think, to remove their nerves is to destroy their capacity to think. I cannot see any reason to suppose thinking is not merely their activity. You appear to be using circular reasoning. You assert that only biological-neuron entities are intelligent, use this assertion to create the set of intelligent entities, and then say that this is valid because to remove the neuro…
It would be circular if it were an argument. I haven't made an argument for it only offered it as the start of our scientific investigation. All observations of brute fact, phrased as arguments, are circular -- because the universe goes unargued for and merely exists. What I mean is this: You and I are having a conversation about consciousness. To do this scientifically we're going to have to point out those things i…
I don’t think I follow you.
> So I shall collect for you all the things we have been talking about when we have said "this is conscious!". And you do the same. And my claim is that everything in this group is in this group... because ... it has a nervous system.
That would be great if we were in 1930 and asking which pre-existing creatures are conscious, but you are asserting that no members of a group which was created to implement all the forms of intelligence that have yet been made quantifiable (as opposed to qualitative judgements of intelligence) are in your set.
I assert that you have a list, and that you have merely defined your words to be a shorthand for that list, rather than made a hypothesis that those words are descriptive properties that allow us to even ask if other things can be in that list, nor to ask if all members of that list truly belong there. (I.e. “is a dog intelligent?”)
For example, you now assert the list is synonymous with “nervous system” (previously “conscious”, previously “intelligent”) without explaining why a digital- or semiconductor-based nervous system would fail your test.
> To believe that an electrified piece of metal could ever belong in the group of things united by their common feature "consciousness" is profound bizarre to me: what exactly is that thing meant to possess that I have?
That’s my question, too. What is that thing which you are meant to posess which supposedly cannot exist on artificial substrates? Why is a biological neuron fundamentally better at thinking than a computer simulation of a biological neuron?
Still, I’m not sure I actually follow what you’re trying to say, because your last three paragraphs seem to be distorted by either autocomplete or google translate. Either way I just cannot extract your point from them.
Re: Deep image prior 'learns' on just one image
#209Earlier 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…
> So if they were to let the training go on for too long the network will produce an exact copy of the distorted input image. It won't. The objective (min (E(x, x0) + R(x)) they are trying to optimize (over output images x) amounts to a combination of: - The output image x should "look like" the input image x0, this is the error term E(x, x0); - The output image x should be "regular", this is the regularization term…
Ah you're totally right about it not producing an exact copy, they even mention how they used different error functions for the different classes of distortion, my mistake.
But w.r.t. the regularization term, what I thought they meant by "we replace the regularizer R(x) with the implicit prior captured by the neural network" at the end of pg 2 was that they let the natural behavior of a neural network during optimization serve as the regularization, without need for an explicit regularization term. Not entirely sure though.
Re: Deep image prior 'learns' on just one image
#210Earlier quoted context omitted.
What do Gabor filters have to do with this?
Parent is saying Gabor filters are typically effectively recapitulated by the first layer of of the network anyway, as they are a natural representation.
But what does have to do with smoothness and translation invariance which this paper is a demonstration of? You even learn Gabor filters with local connectivity without spatial weight sharing.