DALL·E 2 Pre-Training Mitigations

You’ve all seen amazing-looking images like these, entirely generated by an artificial intelligence model. I covered multiple approaches on my channel, like Craiyon, Imagen, and the most well-known, Dall-e 2.

Most people want to try them and generate images from random prompts, but the majority of these models aren’t open-source, which means we, regular people like us, cannot use them freely. Why? This is what we will dive into in this video...

References

►Read the full article: https://www.louisbouchard.ai/how-openai-reduces-risks-for-dall-e-2/
►OpenAI's article: https://openai.com/blog/dall-e-2-pre-training-mitigations/
►Dalle 2 video:
►Craiyon's video:
►Use Craiyon: https://www.craiyon.com/
►My Daily Newsletter: https://www.getrevue.co/profile/whats_ai

Video Transcript

       0:00

you've all seen amazing looking images

0:02

like these entirely generated by an

0:05

artificial intelligence model i covered

0:07

multiple approaches on my channel like

0:09

crayon imogen and the most well-known

0:12

deli 2. most people want to try them and

0:15

generate images from random prompts but

0:18

the majority of these models aren't open

0:20

source which means regular people like

0:23

us cannot use them freely why this is

0:26

what we will dive into in this video

0:29

i said most of them were not open source

0:32

well crayon is and people have generated

0:35

amazing memes using it you can see how

0:38

such a model can become dangerous

0:40

allowing anyone to generate anything not

0:43

only for the possible misuses regarding

0:45

the generations but the data used to

0:47

train such models as well coming from

0:50

random images on the internet pretty

0:52

much anything with questionable content

0:55

and producing some unexpected images the

0:58

training data could also be retrieved

1:00

through inverse engineering of the model

1:02

which is most likely unwanted openai

1:05

also used this to justify not releasing

1:08

the daily2 model to the public here we

1:10

will look into what they are

1:12

investigating as potential risks and how

1:14

they are trying to mitigate them i go

1:16

through a very interesting article they

1:18

wrote covering their data pre-processing

1:21

steps when training dalit ii but before

1:24

so allow me a few seconds to be my own

1:26

sponsor and share my most recent project

1:28

which might interest you i recently

1:31

created a daily newsletter sharing ai

1:34

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1:36

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1:41

subscribe to it on linkedin or with your

1:43

email the link is in the description

1:45

below

1:46

so what does openai really have in mind

1:48

when they say that they are making

1:50

efforts to reduce risks

1:52

first and the most obvious one is that

1:55

they are filtering out violent and

1:57

sexual images from the hundreds of

1:59

millions of images on the internet this

2:02

is to prevent the modal from learning

2:04

how to produce violent and sexual

2:06

content or even return the original

2:08

images as generations it's like not

2:11

teaching your kid how to fight if you

2:13

don't want him to get into fights it

2:15

might help but it's far from a perfect

2:17

fix still i believe it's necessary to

2:20

have such filters in our data sets and

2:22

definitely helps in this case but how do

2:25

they do that exactly they build several

2:27

models trained to classify data to be

2:30

filtered or not by giving them a few

2:32

different positive and negative examples

2:34

and iteratively improve the classifiers

2:37

with human feedback each classifier went

2:39

through our whole data set deleting more

2:42

images than needed just in case as it's

2:44

much better for the model to not see bad

2:47

data in the first place rather than

2:48

trying to correct the shot afterward

2:51

each classifier will have a unique

2:53

understanding of which content to filter

2:56

and will all complement themselves

2:57

ensuring good filtering if by good we

3:00

mean no false negative images going

3:02

through the filtering process

3:04

still it comes with downsides first the

3:07

data set is clearly smaller and may not

3:10

accurately represent the real world

3:12

which may be good or bad depending on

3:14

the use case they also found an

3:16

unexpected side effect of this data

3:18

filtering process it amplified the

3:21

model's biases towards certain

3:23

demographics introducing the second

3:25

thing openai is doing as a pre-training

3:28

mitigation reduce the biases caused by

3:31

this filtering for example after

3:33

filtering one of the biases they noticed

3:36

was that the modal generated more images

3:38

of men and fewer of women compared to

3:41

modals trained on the original data set

3:44

they explained that one of the reasons

3:46

may be that women appear more often than

3:48

men in sexual content which may bias

3:50

their classifiers to remove more false

3:53

negative images containing women from

3:55

the data set creating a gap in the

3:57

gender ratio that the model observes in

4:00

training and replicates to fix that they

4:02

re-weight the filtered data set to match

4:05

the distribution of the initial

4:07

pre-filter data set here is an example

4:10

they cover using cats and dogs where the

4:12

filter will remove more dugs then cats

4:14

so the fix will be to double the

4:16

training loss for images of dogs which

4:19

will be like sending two images of dugs

4:21

instead of one and compensating for the

4:23

lack of images this is once again just a

4:26

proxy for actual filtering bias but it

4:29

still reduces the image distribution gap

4:31

between the pre-filtered and the

4:33

filtered data set

4:35

the last issue is an issue of

4:36

memorization something the models seem

4:39

to be much more powerful than i am as we

4:42

said it's possible to regurgitate the

4:44

training data from such image generation

4:46

models which is not wanted in most cases

4:49

here we also want to generate novel

4:51

images and not simply copy paste images

4:54

from the internet but how can we prevent

4:56

that just like our memory you cannot

4:59

really decide what you remember and what

5:01

goes away once you see something it

5:03

either sticks or it doesn't they found

5:05

that just like humans learning a new

5:07

concept if the model sees the same image

5:10

numerous times in the data set it may

5:12

accidentally know it by heart at the end

5:15

of its training and generate it exactly

5:17

for a similar or identical text prompt

5:20

this one is an easy and reliable fix

5:23

just find out which images are too

5:25

similar and delete the duplicates easy

5:28

doing this will mean comparing each

5:30

image with every other image meaning

5:33

hundreds of quadrillions of image pairs

5:36

to compare instead they simply start by

5:38

grouping similar images together and

5:41

then compare the images with all other

5:43

images within the same and a few other

5:46

clusters around it immensely reducing

5:48

the complexity while still finding 97 of

5:52

all duplicate pairs again another fix to

5:55

do within the data set before training

5:57

our daily model openai also mentions

6:00

some next step they are investigating

6:02

and if you've enjoyed this video i

6:04

definitely invite you to read their

6:06

in-depth article to see all the details

6:08

of this pre-training mitigation work

6:11

it's a very interesting and well-written

6:13

article let me know what you think of

6:15

their mitigation efforts and their

6:17

choice to limit the model's access to

6:19

the public

6:20

leave a comment or join the discussion

6:22

in our community on discord thank you

6:24

for watching until the end and i will

6:26

see you next week with another amazing

6:29

paper

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