Video

Transcript

hi my name is Holly Grimm and I’m an

artist and software developer from Santa

Fe New Mexico and this painting up here

on the upper left here is one of my

original paintings from the Santa Fe

National Forest and I used my tool set

that I’ll be talking about to transform

it into these other images that you see

here artificial intelligence and

creativity how does creativity fit into

a GI one definition of computational

creativity is that it’s a two phase flow

of generation and evaluation first novel

constructs are generated and then they

are evaluated for are based on

meaningfulness and usefulness and here’s

a similar model in reinforcement

learning where actions are generated and

evaluated so for my creativity in art

here’s a list of aesthetic principles

that I got from Dennis Dutton and in

particular I’m interested in applying

style for this project so here’s a

related project that most of us know is

the image style transfer project from

2016 and it demonstrates form in the

form of a lion and composition for my

project instead of image for composition

I’m using eight art composition

attributes that I learned from my art

teacher and here and they’ll be using I

was using the wiki art dataset traina

and so here are some examples of

paintings that that applied to each of

these sorry

to each of these attributes for instance

variety of texture here is a this these

are the low values along the top here it

has very little texture and the lower

lower road has high texture or high

shapes etc four primary color I use the

cyan and yellow magenta color wheel and

here are examples for each color in the

in the wheel and here are six images

with orange primary color showing color

harmony relationships so for instance

these are all the orange is the primary

color in these paintings but they have

various other colors that are also part

of the paintings based on these

different colors relationships so here’s

four different major colors in this

painting here so here’s a diagram of the

network I used I used to resonate 50

Network train free trains on image stats

and each residual block activation is

passed through a global app beverage

cooling layer and then merged into each

of the attributes if you recall from my

previous diagram the lion on the Left

what’s the form but for my tests I use

the cycle game apple - orange data set

and so this is where the a real an apple

is generated and I mean an orange is

translated or a fake orange is generated

from the Apple and then reconstructed

back in addition to the classic cycle

gam AUSA’s I added I passed this

translated Orange

my Aitken network along with some target

attribute values and and then I came up

with some losses based on that and so

here’s some example results

here’s color harmony after passing the

Apple in and passing in hi analogous

value it was able to create a analogous

color wheel basically and then here’s

another example here for a complementary

color we’re out of the leaf and the

background were changed to blue cyan and

I have another example as a variety of

color the left is was an image with a

lot of color here and it was translated

into a monochromatic red color on the

right shows the opposite case where

there were many it’s kind of hard to see

on the slide but there were many colors

that were generated and even with the

relatively small dataset of 500 label

wiki labelled wiki art images I was able

to train on these eight art

compositional attributes via the cycle

again plus the a can network and get

some pretty interesting results so

possible next steps would include

applying activation mapping to

understand how the different

compensation ’el attributes are working

here’s an example from learning

photography aesthetics from 2017 and it

would also be interesting to replace my

cycle again at work with other form

generation strategies like opening eyes

2018 project glow where they generated

these images of faces and bedrooms and

in another cool project from 2015 was

where an IRL robotic at rope robotic

Ayden’s generated the actual physical

paintings here on the right using a

inverse reinforcement

you could find my blog posts for this

project on my website and the code is on

github and again here’s another painting

of mine in the upper left-hand corner

with some of the generations that I got

from my network I’d like to thank open

AI and in particular of my mentor

Christie and Larissa and the rest of my

scholars thank you so much okay so many

questions

yeah so I I basically just did a merge

on those on the from the global average

bullying did emerge right into that and

that’s actually I think learning from

photography aesthetics is a is where I

got that particular method of doing that

any other

and I’m always available over here later

for questions

[Applause]