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It's surprising how poorly labeled these images are; who is curating this collection? Can't they crowd-source a proper labeling project - I wonder how much bet

It's surprising how poorly labeled these images are; who is curating this collection? Can't they crowd-source a proper labeling project - I wonder how much better things like Stable Diffusion would be if its training would include correct, complete labels for the images. I'm sure lots of folks would willingly spend a few minutes here and there to aid with the labeling if it means they get to enjoy the model for free.

simandl 4y ago
This is Laion-5B, you can read more about it here: https://laion.ai/blog/laion-5b/ https://laion.ai/blog/laion-5b/ Imagen and Stable-Diffusion both used subsets of this full 5.8B image set.
- yreg 4y ago
  Is Imagen actually trained on a subset of Laion-5B and nothing else? I've heard they used huge internal data sets.
  - simandl 4y ago
    They have their own datasets and included Laion-400M, a subset of 5b that was released prior to 5b. You can see a short explanation in imagen's "Limitations and Societal Impact" section at: https://imagen.research.google/ https://imagen.research.google/. > While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes.
orbital-decay 4y ago
If you aren't Google, manually doing that with 5+ billion images might prove difficult, to put it mildly. Large-scale labeling is typically bootstrapped with smaller models and whatever manual data you have. What's being curated is the bootstrapping process.
- adolph 4y ago
  Well, imagine a Wikipedia like org/effort.
  - NavinF 4y ago
    That exists for anime style images in the form of Danbooru: https://www.gwern.net/Danbooru2021 https://www.gwern.net/Danbooru2021 Other mediums don't have enough people interested in curating and labelling 5 million images with very detailed tag lists for free.
  - mgdlbp 4y ago
    https://commons.wikimedia.org/ https://commons.wikimedia.org/
0xrisk 4y ago
part of our idea is that artists can help with labelling their own work
- UncleEntity 4y ago
  The same artists who have been pitching a fit about how the robots are coming for their jobs? Pretty sure they are trying to figure out a way their art can’t be used to train an AI and won’t be willingly participating in making it easier.
  - simandl 4y ago
    You might be surprised. We have almost as many opt-in requests as opt-outs since we announced this today. We don't see this as binary in the long term. Maybe artists want to release art from a prior period, for example, but withhold their current series until they move to the next.
    - p_hoep 4y ago
      I think we'll see people who want to post images of themselves in there as well. Like, being a 'playable character' you could prompt for to be in the artwork could be pretty fun.
    - NicoleJO 4y ago
      Why is opt out a thing? Who told you you could use the data in the first place? It's copyrighted. Reproducing any of it is a crime.
      - simandl 4y ago
        Reading through your comments, it appears you're under the impression that we (Spawning) trained models using this data. That is not the case. We're building tools to help people to remove themselves from, or add themselves to, datasets used (by others) to train these models. We're hoping to make that as close to 0 effort as possible for all parties. We agree that artists should control how their works are used, and we're working hard to make that the norm.
XorNot 4y ago
The training used in the current round of image generators is Contrastive learning - the idea is that you don't use "properly curated" datasets - you just throw volume at the problem and let it sort itself out. Which makes sense - humans learn by the same mechanism. The LAION-5B set in particular is filtered by it's CLIP embeddings - basically run a better AI to classify the images, and check that the two descriptions rate highly enough for similarity, which in turn lets the system have some confidence an image isn't completely wrong (also the step you filter out NSFW and other things if you want, generally).
ma2rten 4y ago
Those images are not really "labeled". They just scraped the alt text. A lot of the recent advances in AI have been by using lower quality large scale web data, instead of hand labeling. The noise will average out. Hand labeled data can be used for finetuning.
- Kye 4y ago
  This is the same thinking that leads to incredible astrophotography using stacked long exposure images that appear to be pure noise individually.
  - thfuran 4y ago
    Not really. There's sound theoretical basis for that approach in astrophotography. The sort of bias and or noise introduced by systemically poor labeling is much less reliably averaged out.
freefruit 4y ago
Seems it's related to what alt tags the images are using.
throwaway0x7E6 4y ago
if there are any researchers reading this, I'd like to introduce you to booru porn galleries like rule34. millions of images meticulously labelled by dedicated enthusiasts
- FeepingCreature 4y ago
  https://www.gwern.net/Danbooru2021 https://www.gwern.net/Danbooru2021
- newswasboring 4y ago
  There are already trained models by the community on these datasets. It's called Waifu Diffusion [1] (not 100% if this is the most popular model). [1] https://huggingface.co/hakurei/waifu-diffusion https://huggingface.co/hakurei/waifu-diffusion
mattigames 4y ago
Could you imagine if the mediocre results we currently get from "AI" were mostly from poorly labeled data in every huge dataset and not lack of a scientific or technological breakthrough?, it wouldn't be the first time that too many academics are blinded by the wishful thinking that what they need is an eureka moment when what is needed is tons of dull repetitive work. So for instance for photography generation what may be needed is huge amount of clean photos with obsessively detailed labels, maybe just the exact same single-point-lighting (the exact coordinates of the light being a data point, plus strength/lumens), with 8 pictures of each subject in black background: front, back, left, right, top, bottom, 3/4 mostly-front (AKA the corner), 3/4 mostly-back, and then the same 8 ones but with white background, then also include all the info possible: weight, height, width and depth, plus versions of the most common states of each object (ball: inflated or deflated; bird: flying, idle or walking), plus photos adding two subjects together (one dataset of woman with hat, another wearing the same clothes but without the hat, and one of just the hat without the woman), with properly labeled relationships so it's clear they all refer to the exact same hat (or lack of), you get the idea... Then the most important thing for "image generation AI" may not be computing power but the most boredom-resiliant staff you can hire; of course that's just an example for photography, for things like text you may need an equivalent rigorous effort by a multitude of linguists.
- prox 4y ago
  I think for human generation you need to add a bone/skinmesh generator in 3d. So it needs to learn what the position is of the body in a 3d space and compare with the source images.
samus 4y ago
Google has been doing this for years with their various iterations of captchas.
newswasboring 4y ago
So stable Diffusion has a img2prompt mode. I wonder if that can be used somehow. The prompts it has yielded for my personal images have been very descriptive and good. It would be interesting to see how different the img2prompt output is for the training images. I would love to measure it but I don't even know how to calculate this distance.
- aaimnr 4y ago
  That would be circular, no new information would be provided to the model.
macrolime 4y ago
I don't think that's feasible for anyone but someone like Google or another tech giant. They could use Wikimedia Commons to get a smaller collection of better labeled images. Currently it's images from Common Crawl extracted by Laion, not sure if it already includes Wikimedia Commons.
okl 4y ago
You're right. I just searched for "milk" and was not expecting the NSFW results...
- tcmb 4y ago
  I wouldn't call this poorly labeled, 'milk' and 'breasts' actually are semantically very close.
poulpy123 4y ago
labeling 6 billions pictures, even at 10 labels per second would take 20 years, so I guess nobody
- yreg 4y ago
  Could be done by reCAPTCHA, although might be more difficult to classify the results (whether a user passed the test) compared to their usual challenges.
  - DataWraith 4y ago
    Not quite a CAPTCHA, but something similar has actually been done before. The ESP game[1] paired two random people looking at the same image while a timer ticked down. The players had to enter labels that described the image, and if both players applied the same label, their score increased, and the label was associated more strongly with that image. Well-known labels for an image were excluded after a while, so you had to guess less and less obvious labels in order to score as time went by. Once in a while the system would also assign test images with known labels to prevent cheating. Apparently a lot of people played it because it was fun, so there wasn't even the need to pay them for labeling the dataset... [1]: https://en.wikipedia.org/wiki/ESP_game https://en.wikipedia.org/wiki/ESP_game
hilbert42 4y ago
"Can't they crowd-source a proper labeling project" I'm very surprised how primitive it is. I put in a few names of uncommon technical objects and some of its images were close and others so far off as to be unrecognizable or totally unrelated/useless. What it needs is a quick/instant feedback system that allows humans to rate an image against the query word. A rating scale of say 1 to 5 where 1 is an identical match, 2 a close match, 3 marginal, 4 ambiguous, 5 wrong/totally unrelated. If in place then likely thousands would make an effort to optimize the matching.

simandl 4y ago

This is Laion-5B, you can read more about it here: https://laion.ai/blog/laion-5b/ https://laion.ai/blog/laion-5b/ Imagen and Stable-Diffusion both used subsets of this full 5.8B image set.

yreg 4y ago
Is Imagen actually trained on a subset of Laion-5B and nothing else? I've heard they used huge internal data sets.
- simandl 4y ago
  They have their own datasets and included Laion-400M, a subset of 5b that was released prior to 5b. You can see a short explanation in imagen's "Limitations and Societal Impact" section at: https://imagen.research.google/ https://imagen.research.google/. > While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes.

orbital-decay 4y ago

If you aren't Google, manually doing that with 5+ billion images might prove difficult, to put it mildly. Large-scale labeling is typically bootstrapped with smaller models and whatever manual data you have. What's being curated is the bootstrapping process.

adolph 4y ago
Well, imagine a Wikipedia like org/effort.
- NavinF 4y ago
  That exists for anime style images in the form of Danbooru: https://www.gwern.net/Danbooru2021 https://www.gwern.net/Danbooru2021 Other mediums don't have enough people interested in curating and labelling 5 million images with very detailed tag lists for free.
- mgdlbp 4y ago
  https://commons.wikimedia.org/ https://commons.wikimedia.org/

0xrisk 4y ago

part of our idea is that artists can help with labelling their own work

UncleEntity 4y ago
The same artists who have been pitching a fit about how the robots are coming for their jobs? Pretty sure they are trying to figure out a way their art can’t be used to train an AI and won’t be willingly participating in making it easier.
- simandl 4y ago
  You might be surprised. We have almost as many opt-in requests as opt-outs since we announced this today. We don't see this as binary in the long term. Maybe artists want to release art from a prior period, for example, but withhold their current series until they move to the next.
  - p_hoep 4y ago
    I think we'll see people who want to post images of themselves in there as well. Like, being a 'playable character' you could prompt for to be in the artwork could be pretty fun.
  - NicoleJO 4y ago
    Why is opt out a thing? Who told you you could use the data in the first place? It's copyrighted. Reproducing any of it is a crime.
    - simandl 4y ago
      Reading through your comments, it appears you're under the impression that we (Spawning) trained models using this data. That is not the case. We're building tools to help people to remove themselves from, or add themselves to, datasets used (by others) to train these models. We're hoping to make that as close to 0 effort as possible for all parties. We agree that artists should control how their works are used, and we're working hard to make that the norm.

XorNot 4y ago

The training used in the current round of image generators is Contrastive learning - the idea is that you don't use "properly curated" datasets - you just throw volume at the problem and let it sort itself out. Which makes sense - humans learn by the same mechanism. The LAION-5B set in particular is filtered by it's CLIP embeddings - basically run a better AI to classify the images, and check that the two descriptions rate highly enough for similarity, which in turn lets the system have some confidence an image isn't completely wrong (also the step you filter out NSFW and other things if you want, generally).

ma2rten 4y ago

Those images are not really "labeled". They just scraped the alt text. A lot of the recent advances in AI have been by using lower quality large scale web data, instead of hand labeling. The noise will average out. Hand labeled data can be used for finetuning.

Kye 4y ago
This is the same thinking that leads to incredible astrophotography using stacked long exposure images that appear to be pure noise individually.
- thfuran 4y ago
  Not really. There's sound theoretical basis for that approach in astrophotography. The sort of bias and or noise introduced by systemically poor labeling is much less reliably averaged out.

freefruit 4y ago

Seems it's related to what alt tags the images are using.

throwaway0x7E6 4y ago

if there are any researchers reading this, I'd like to introduce you to booru porn galleries like rule34. millions of images meticulously labelled by dedicated enthusiasts

FeepingCreature 4y ago
https://www.gwern.net/Danbooru2021 https://www.gwern.net/Danbooru2021
newswasboring 4y ago
There are already trained models by the community on these datasets. It's called Waifu Diffusion [1] (not 100% if this is the most popular model). [1] https://huggingface.co/hakurei/waifu-diffusion https://huggingface.co/hakurei/waifu-diffusion

mattigames 4y ago

Could you imagine if the mediocre results we currently get from "AI" were mostly from poorly labeled data in every huge dataset and not lack of a scientific or technological breakthrough?, it wouldn't be the first time that too many academics are blinded by the wishful thinking that what they need is an eureka moment when what is needed is tons of dull repetitive work. So for instance for photography generation what may be needed is huge amount of clean photos with obsessively detailed labels, maybe just the exact same single-point-lighting (the exact coordinates of the light being a data point, plus strength/lumens), with 8 pictures of each subject in black background: front, back, left, right, top, bottom, 3/4 mostly-front (AKA the corner), 3/4 mostly-back, and then the same 8 ones but with white background, then also include all the info possible: weight, height, width and depth, plus versions of the most common states of each object (ball: inflated or deflated; bird: flying, idle or walking), plus photos adding two subjects together (one dataset of woman with hat, another wearing the same clothes but without the hat, and one of just the hat without the woman), with properly labeled relationships so it's clear they all refer to the exact same hat (or lack of), you get the idea... Then the most important thing for "image generation AI" may not be computing power but the most boredom-resiliant staff you can hire; of course that's just an example for photography, for things like text you may need an equivalent rigorous effort by a multitude of linguists.

prox 4y ago
I think for human generation you need to add a bone/skinmesh generator in 3d. So it needs to learn what the position is of the body in a 3d space and compare with the source images.

samus 4y ago

Google has been doing this for years with their various iterations of captchas.

newswasboring 4y ago

So stable Diffusion has a img2prompt mode. I wonder if that can be used somehow. The prompts it has yielded for my personal images have been very descriptive and good. It would be interesting to see how different the img2prompt output is for the training images. I would love to measure it but I don't even know how to calculate this distance.

aaimnr 4y ago
That would be circular, no new information would be provided to the model.

macrolime 4y ago

I don't think that's feasible for anyone but someone like Google or another tech giant. They could use Wikimedia Commons to get a smaller collection of better labeled images. Currently it's images from Common Crawl extracted by Laion, not sure if it already includes Wikimedia Commons.

okl 4y ago

You're right. I just searched for "milk" and was not expecting the NSFW results...

tcmb 4y ago
I wouldn't call this poorly labeled, 'milk' and 'breasts' actually are semantically very close.

poulpy123 4y ago

labeling 6 billions pictures, even at 10 labels per second would take 20 years, so I guess nobody

yreg 4y ago
Could be done by reCAPTCHA, although might be more difficult to classify the results (whether a user passed the test) compared to their usual challenges.
- DataWraith 4y ago
  Not quite a CAPTCHA, but something similar has actually been done before. The ESP game[1] paired two random people looking at the same image while a timer ticked down. The players had to enter labels that described the image, and if both players applied the same label, their score increased, and the label was associated more strongly with that image. Well-known labels for an image were excluded after a while, so you had to guess less and less obvious labels in order to score as time went by. Once in a while the system would also assign test images with known labels to prevent cheating. Apparently a lot of people played it because it was fun, so there wasn't even the need to pay them for labeling the dataset... [1]: https://en.wikipedia.org/wiki/ESP_game https://en.wikipedia.org/wiki/ESP_game

hilbert42 4y ago

"Can't they crowd-source a proper labeling project" I'm very surprised how primitive it is. I put in a few names of uncommon technical objects and some of its images were close and others so far off as to be unrecognizable or totally unrelated/useless. What it needs is a quick/instant feedback system that allows humans to rate an image against the query word. A rating scale of say 1 to 5 where 1 is an identical match, 2 a close match, 3 marginal, 4 ambiguous, 5 wrong/totally unrelated. If in place then likely thousands would make an effort to optimize the matching.