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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

#51

Post by [deleted] »

nerfhammer wrote: post #34
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).

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?

[deleted]

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

#52

Post by mvij »

nerfhammer wrote: post #34
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).

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?

Handcrafted, but auto optimizing is a hot research topic right now with DeepMind et al. Needs to be evolved at deeper level than what they’re doing now so architecture is discovered, not just optimized.

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

#53

Post by killjoywashere »

The intel and espionage communities are going to be all over this. This makes the Soviet photo retouching look like child's play.

Let's say you want to start a war, and need some evidence of chemical weapons. Now, you can drop in some images of chemical weapons and claim a GAN found them. Sample press releases:

"We believe this photo was retouched to hide the chemical weapons. Using a GAN, we recovered clear photographic proof of the chemical weapons."

"We believe they are using this tin-roofed building to hide the chemical weapons. Using a GAN on our own satellite images, we recovered clear photographic proof of the chemical weapons."

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

#56

Post by jboggan »

flyingspork wrote: post #33
JumpCrisscross wrote: post #24

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…

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.

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

#57

Post by nerfhammer »

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.

then the results of this paper are sort of surprising that it's that good, if the structure is just relatively old, hand-crafted and traditional.

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

#59

Post by BatFastard »

jmmcd wrote: post #28
knolan wrote: post #20

Earlier quoted context omitted.

Somewhat similar to content aware fill in Photoshop [0]. The untrained network can latch onto frequent patterns and match them to holes in the data. Why doesn’t it paint everything white? Are these actually transparent images or are they somehow tagged? [0] https://helpx.adobe.com/photoshop/using/content-aware-patch-...

Yes, for the inpainting, the parts to be painted (big white deleted areas) are supplied as masks, so it doesn't try to match them.

But its generating unique content in those areas...

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

#60

Post by albertTJames »

ok.. this is revolutionary. Using the architecture as a way to capture an image prior hints at how network structure and captured invariance are related. By analogy it leads to thinking of brain areas as both hard coded prior knowledge through their natural arrangement and plastic learning structure. Turning the problem on its head shines a new light to how we could conceive network architectures.
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