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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

#41

Post by theoh »

azeirah wrote: post #12
BatFastard wrote: post #10

How can it possibly know what was in the white areas of the library? Is there a residual image? Seems impossible that it guesses correctly.

It doesn't guess "correctly" at all. Zoom in on the image, and focus on the filled-in areas, they look really blurry. It just doesn't look very bad from a birds-eye view.

It seems like it should be similar in capability to a wavelet-based approach to e.g. image inpainting. In other words the neural network architecture essentially establishes a basis of features onto which the image is projected. Gabor wavelets are known to occur in the human visual system as the 'image elements' in a similar setup -- Gabor wavelets are kind of optimal, but clearly the features that this nn architecture uses are pretty effective too.

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

#42

Post by [deleted] »

cs702 wrote: post #35
nerfhammer wrote: post #34

Earlier quoted context omitted.

is the structure of these CNNs learned or designed? Do they run some kind of optimizer to learn the optimal CNN structure or does some person sit down and pick structures to include in it?

Convolutional layers are designed, by and large, and they're mostly the same everywhere. Yann Le Cun came up with them in the mid-90's, but their academic origins go back to at least the 50's and 60's.

[deleted]

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

#43

Post by JumpCrisscross »

cutcss wrote: post #40
JumpCrisscross wrote: post #24

Can someone break this down for this layman?

Instead of looking for patterns from itself on others images (training data) it starts with noise and deforms such noise based on patterns found on itself and favors deformations closer to the input image; eventually reaching something close to the input image without the noise (cause the noise it's pattern-less or at least weak enough to die over stronger patterns)

Does this say more about CNNS fundamentally or our design process for their structure? What sort of assumptions might one make when "structuring" a CNN?

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

#44

Post by felippee »

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…

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 looking in-paint.

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

#45

Post by narrator »

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 brain probably has a lot of hard wired networks to recognize faces and to not like being cold for example. I think even feral children have these capabilities.

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

#46

Post by ska »

cs702 wrote: post #31
JumpCrisscross wrote: post #24

Can someone break this down for this layman?

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).

Which in turn relies on older work.

I wonder if anyone has looked at what Mumford-Shaw implications would look like projected onto a CNN?

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

#47

Post by ska »

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.

Creating is perhaps better states as “asserting”

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

#49

Post by [deleted] »

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…

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