CAN: Generating Art by Learning About Styles and Deviating from Style Norms | Hacker News Reader

CAN: Generating Art by Learning About Styles and Deviating from Style Norms

arxiv.org

37 pointsrimraf9 comments

Art is the soul embedded in the medium but it's not the medium itself. So art could be anything digital if it has some human touch. But is art produced by "generating [...] by learning about styles and deviating from them" really art? I think it is but a new kind of art. Nature can produce art but algorithms are not nature. It is very interesting to observe where this is going. So far generated art had some kind of wow moment but people quickly got bored of it until a new formula is discovered, has its moment then fades. This type of generated art through learning about styles has a lot of potential to change what we see as art. End of rant.

I am surprised there are not more "GANs-as-a-service" startups. I only know of runwayml.com. GANs seem like there are ripe opportunities for collaborations with artists. But, inaccessible to artists without easier tools.

Well, artists aren't known for having lots of money to spend :-)

I think you might need to be selling the output or something too. As well as wall art, it could be wallpaper, carpet, fabric, wrapping paper, etc. If you could make it less abstract but "right"-looking, you could generate stock art backgrounds.

What do you mean by GANs-as-a-service here? I'm not sure that our understanding of GANs is refined enough to offer something like "a framework for letting non-experts train a GAN to do cool things" outside of a very limited scope - there are lots of issues with things like making sure that the generator/discriminator converge and avoiding oscillations back and forth between either winning, that can unexpectedly ruin your training.

On the other hand, if you mean "startups that use GANs to build cool systems, such as manipulable latent spaces for interesting data" then I totally agree! For example, we're using GANs to train voice conversion systems, which let you manipulate a space of voice representations to make new voices, which you can then sound like. I definitely expect to see a lot of startups doing interesting things like that popping up!

Generative art is my hobby and I know about these advances in AI so I would really love a simple tool for this. Maybe an SDK or something. I only have a laptop with a shitty GPU so there's no way I can train an run my own models.

If there were a tool where I can just patch together random models that would be definitely interesting.

It seems like GANs (from my perspective as a layperson) are the best application of neural nets to take an input and generate a very creative output. From the small set of papers I've read, it seems like we are converging upon some interesting general applications of GANs that could be turned into services. But, it sounds like you don't think that's the case, and if that's so, that explains why there are not more GaaSes. Or, maybe it is because of that acronym. :)

So, in a nutshell, it seems like the code isn't generalized enough that the output could be predictable enough to make public and useful as a service. Right? I do feel like every time I dip my toes into playing with a GAN, I do get lost in the setup, and I wish it were easier, hence my need for a service to do the art projects I want to build.

> there are lots of issues with things like making sure that the generator/discriminator converge and avoiding oscillations back and forth between either winning, that can unexpectedly ruin your training.

I think the situation has been gradually improved. I've been messing around with GANs since Soumith first released his DCGAN and I've tried out DCGAN, WGAN (in several variants), SAGAN, MSG-GAN, ProGAN etc, and GANs increasingly 'just work'. ProGAN especially: I've yet to see any of my ProGANs diverge or have serious instability, and I've been doing things like retraining the CelebHQ-A faces model with anime faces, and it's worked just fine (better than any other GAN so far, actually). ProGAN is slow but the progressive training works wonders for stability and image generation quality. I haven't had to tweak the learning rate or mess with filters or any of the things necessary with earlier GANs.

It might be expensive to offer as a service because you are still talking weeks of training on a GPU with >=10GB VRAM (so a 1080ti or P100 instance), but I think you could offer it as a service where the artist merely uploads a tarball of images and gets out a trained model and samples without much more expertise.

Can I ask, what are you using to play with these GANs? An online playground like colab from Google? Or, your own hardware? Are you using a shared notebook, or building them up yourself? I glanced at your blog/site so sorry if I missed a description of your research playground already. Thank you for the rich comment.

I didn't have a GPU when I started; I started with a borrowed GTX 980 I sshed into, but concluded that DCGAN/WGAN were inadequate to generate decent anime faces, much less anime images in general.

So I took a very long break to work on creating a large anime image corpus ( https://www.gwern.net/Danbooru2017 ), with the idea that the rich tagging information could fix the GAN problems. StackGAN impressed me a great deal by generating high-quality images quickly by feeding in a text embedding of the image description and then doing progressive training, so I figured that Danbooru tags would be almost as good. (I couldn't get StackGAN itself to work even though I had tags downloaded for preliminary versions of Danbooru2017: it uses an old pre-generated set of text embeddings and I wasn't good enough at Python/Tensorflow to either fix the original embedding code to generate a new set, or create a new embedding & edit StackGAN to use that.)

Two months ago or so, I finished building my desktop with 2x1080ti, and I've been using a spot P100 AWS instance as well to run 2 or 3 instances of MSG-GAN/ProGAN/SAGAN/Glow simultaneously. The upshot there so far is: MSG-GAN works well but simply requires too much RAM since it doesn't use progressive growing; SAGAN works nicely but the self-attention mechanism means it has a hard time scaling past 128px; Glow has very impressive results in OpenAI's paper but the memory consumption is enormous (reversible gradients or no) and the paper doesn't make clear that it requires several GPU-months and is much slower than ProGAN, so I never got beyond 'cool textures' before I began to wonder if I had a bug and checked the paper/README much more carefully; ProGAN works well but is still relatively slow (1-2 GPU-weeks for anime faces from scratch of decent quality, ~3 GPU-days if initializing from CelebHQ-A for transfer learning) and can handle faces easily but handling anime images in general may require several GPU-months of training, I'm not sure, since I only gave it about a month before moving on.

I post samples from various runs occasionally on my Twitter thread: https://twitter.com/gwern/status/1040323921961213957

I mostly work in the terminal, no fancy notebook stuff. I should probably write it all up, but you know how it is, it's a pain to figure out what you were doing years ago and figure out what samples survive etc.

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