Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Deep image prior 'learns' on just one image

dmitryulyanov.github.io

Post reply on HN
Print view View original

61–70 of 235 posts

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

#61

Post by [deleted] »

jmmcd wrote: post #30
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…

> 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…

[deleted]

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

#62

Post by dwaltrip »

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).

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 with the single distorted image.

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

#63

Post by andbberger »

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.

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

#64

Post by fizixer »

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).

By structure, do you mean the architecture of the NN?

Meaning number of hidden layers, nodes per layer, and their connectivity with each other and with the input and output layers?

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

#66

Post by cs702 »

fizixer wrote: post #64
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, do you mean the architecture of the NN? Meaning number of hidden layers, nodes per layer, and their connectivity with each other and with the input and output layers?

No, the paper means primarily the weight sharing in each kernel filter within each convolutional layer, and the stacking of these layers in deep networks.

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

#67

Post by EGreg »

cs702 wrote: post #32
jmmcd wrote: post #30

Earlier quoted context omitted.

> 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…

Thanks. I'm not mixing them up! I'm just wondering whether and to what degree architecture , i.e., network structure, will prove important for other, more advanced AI tasks, including up to AGI.

https://en.m.wikipedia.org/wiki/Language_acquisition_device

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

#68

Post by zo7 »

I feel like this is related to the information bottleneck idea that's been floating around for some time [1]. I only really understand both of these at a superficial level, but from what I think I understand one thing that they observed is that there are two phases when training a deep learning model: a phase which maximizes the mutual information (?) between the input and the output, and a compression phase which compresses the learned representation. In that light this work makes sense, since artifacts are essentially noise that the network would filter out in the fitting process.

Very cool work though.

[1]: https://youtu.be/bLqJHjXihK8

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

#69

Post by eanzenberg »

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?
Post reply on HN