# Art Composition Attributes + CycleGAN | Holly Grimm | OpenAI Scholars Demo Day 2018

## Метаданные

- **Канал:** OpenAI
- **YouTube:** https://www.youtube.com/watch?v=KisG5I0DcpY
- **Дата:** 02.07.2020
- **Длительность:** 7:03
- **Просмотры:** 2,303
- **Источник:** https://ekstraktznaniy.ru/video/11598

## Описание

Holly Grimm talks about Art Composition Attributes + CycleGAN on OpenAI Scholars Demo Day on September 20, 2018. 

Learn more: https://openai.com/blog/openai-scholars-2018-final-projects#holly

## Транскрипт

### <Untitled Chapter 1> []

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

### Aesthetic Principles [1:03]

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

### Image Style Transfer Project [1:15]

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 applied to each of these sorry 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 road has high texture or high shapes etc four primary color I use the

### Primary Color [2:10]

cyan and yellow magenta color wheel and here are examples for each color 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 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 then I came up with some losses based on that and so

### Example Results [3:52]

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 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