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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

#71

Post by joshmarlow »

oh_sigh wrote: post #26
JumpCrisscross wrote: post #24

Can someone break this down for this layman?

Neural networks are powerful without even training them. By merely designing the structure of them you are creating something.

This kind of reminds me of what I took away from Gerald Edelman's work ([0]).

The gist as I remember it was that he theorized that the cortex is composed of assemblies of neurons formed in development (in the womb, potentially with epi-genetic factors). Each assembly, just by chance of it's structure, is likely good at something. Over time, assemblies take up the tasks they happen to be good at and further specialize with experience.

[0] - https://en.wikipedia.org/wiki/Neural_Darwinism

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

#72

Post by nl »

sarreph wrote: post #15
cdumler wrote: post #5

Red Dwarf - "Uncrop" - http://www.dailymotion.com/video/x2qlmuy

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...

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

#73

Post by [deleted] »

cs702 wrote: post #23
bitL wrote: post #4

Wow, so just the weight sharing architecture does so much already? I am wondering if the same could be done with LSTMs on sequences or CNNs on voice...

I'm wondering the same thing too. Note also that this finding strongly suggests that neural net architecture actually is quite important, possibly even more important than having more data -- which contradicts the conventional wisdom!

[deleted]

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

#74

Post by gbrown »

So what kind of generative stuff does it "learn" from really really sparse data, or from random noise? Trained generative models can produce some crazy things, I wonder if something similar could work here.

Recursive geometric patterns perhaps?

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

#76

Post by candiodari »

dwaltrip wrote: post #62
cs702 wrote: post #31

Earlier quoted context omitted.

The structure of convolutional neural nets specifies much of the prior knowledge necessary for learning. In other words, the design of these neural nets makes a lot of correct assumptions about the nature of images (stationarity of pixel statistics, locality of pixel dependencies, and so on).

By structure, we are simply referring to the number of layers, the number of neurons in each layer, and the specific connections between neurons in each pair of neighboring layers, right? So in this paper, they carefully chose a certain structure, set the weights randomly, and then what happened after that? I understand that they did not then train it with a training data set, but I'm not quite getting what they did…

Well given that it's CNNs, you're leaving out weight sharing.

So by structure you should also include the demand that the prediction of any NxN patch of the image should be roughly equal to the prediction of any other NxN patch of the image.

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

#77

Post by jacquesm »

cs702 wrote: post #23
bitL wrote: post #4

Wow, so just the weight sharing architecture does so much already? I am wondering if the same could be done with LSTMs on sequences or CNNs on voice...

I'm wondering the same thing too. Note also that this finding strongly suggests that neural net architecture actually is quite important, possibly even more important than having more data -- which contradicts the conventional wisdom!

There is some pretty strong evidence for this: all the toddlers in the world. You only need to show them something once and they'll immediately be able to recognize more examples of the same thing from different angles and even when it is partially hidden. All they have to guide them is the structure of their brains, not the quantity of data they have been exposed.

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

#78

Post by jacquesm »

theoh wrote: post #36
oh_sigh wrote: post #26

Earlier quoted context omitted.

Neural networks are powerful without even training them. By merely designing the structure of them you are creating something.

Truly, an occasion for this koan: Sussman attains enlightenment In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. “Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut hi…

If there ever was one this is it.

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

#80

Post by sanxiyn »

eanzenberg wrote: post #69
cs702 wrote: 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…

The “task dependent observation model” also comes out of thin air, or is it trained with lots of data?

Observation model is trivial and data-free. For example, just mean squared error for denoising, mean squared error of downscaled image for super-resolution, etc.
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