Here’s how I would do art with machine learning if I had to

June 6, 2016 — February 1, 2022

computers are awful
generative art
machine learning
making things
neural nets
photon choreography

I’ve a weakness for ideas that give me plausible deniability for making generative art while doing my maths homework.

NB I have recently tidied this page up but the content is not fresh; there is too much happening in this field to document.

Figure 1: Quasimondo: so do you.

This page is more chaotic than the already-chaotic median, sorry. Good luck making sense of it. The problem is that this notebook is in the anti-sweet spot of “stuff I know too much about to need notes but not working on enough to promote”.

Some neural networks are generative, in the sense that if you train ’em to classify things, they can also predict new members of the class. e.g. run the model forwards, it recognizes melodies; run it “backwards”, it composes melodies. Or rather, you maybe trained them to generate examples in the course of training them to detect examples. There are many definitional and practical wrinkles, and this ability is not unique to artificial neural networks, but it is a great convenience, and the gods of machine learning have blessed us with much infrastructure to exploit this feature, because it is close to actual profitable algorithms. Upshot: There is now a lot of computation and grad student labour directed at producing neural networks which as a byproduct can produce faces, chairs, film dialogue, symphonies and so on.

Perhaps other people will be more across this?

Oh and also google’s AMI channel, and ml4artists, which publishes sweet machine learning for artists topic guides.

There are NeurIPS streams about this now.

Figure 2

1 Visual synthesis

There is a lot going on, which I should triage. Important example: Maybe I should also do generative art with neural networks.

1.1 AI image editors

See image editing with aI.

1.2 Style transfer and deep dreaming

You can do style transfer a number of ways, including NN inversion and GANs.

See those classic images from google’s tripped-out image recognition systems). Here’s a good explanation of what is going on.

  • deep art:

    Our mission is to provide a novel artistic painting tool that allows everyone to create and share artistic pictures with just a few clicks. All you need to do is upload a photo and choose your favorite style. Our servers will then render your artwork for you.

  • For example, OPEN_NSFW was my favourite (NSFW).

  • Differentiable Image Parameterizations looks at style transfer with respect to different decompositions of the image surface. (There is stuff to follow up about checkerboard artefacts in NNs which I suspect is generally important.)

  • Self-Organising Textures stitches these two together, using a VGG discriminator as a loss function for training textures.

  • Deep dream generator does the classic deep-dreaming style perturbations

  • fast style transfer.

1.3 GANs

Clever uses of GANs that are not style transfer.

Figure 3

1.4 Incoming

2 Text synthesis

At one point, Ross Gibson’s Adventures in narrated reality was a state-of-the-art text generation using RNNs. He even made a movie of a script generated that way. But now, massive transformer models have left it behind technologically, ff not humorously.

3 Music

3.1 Symbolic composition via scores/MIDI/etc

See ML for composition.

3.2 Audio synthesis

See analysis/resynthesis, voice fakes.

4 References

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