I’ve a weakness for ideas that give me plausible deniability for making generative art while doing my maths homework.
This page is more chaotic than the already-chaotic median, sorry. Good luck making sense of it.
See also analysis/resynthesis.
Many neural networks, are generative in the sense that even 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 here, and this quality 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 very 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.
Some as-yet-unfiled neural-artwork links I should think about.
- So simple it’s cute, CPPNs are probably what Jonathan McCabe has been producing for years.
- iGAN, iGAN: Interactive Image Generation via Generative Adversarial Networks
- interpolating style transfer.
- neurogram is a compact semi-untrained neural network image synthesis-in-the-browser
- Adversarial generation is a cool hack if you hate boring stuff like labelling data sets e.g. chair generation
- Autoencoding beyond pixels using a learned similarity metric (Larsen et al. 2015). code The clever hack here is using generative adversarial networks which is obviously rich in potential.
- Ross Gibson Adventures in narrated reality gives an overview of text generation using RNNs.
Alex Graves, Generating Sequences With Recurrent Neural Networks, generates handwriting. Relatedly, sketch-rnn is reaaaally cute.
Distill.pub has some lovely visual explanations of visual and other neural networks.
hardmaru presents an amazing introduction to running sophisticated neural networks in the browser, targeted at artists, which goes over the handwriting post in a non-technical way.
a platform for creators of all kinds to use machine learning tools in intuitive ways without any coding experience. Find resources here to start creating with RunwayML quickly.
In particular it plugs into Blender and Photoshop and allows you to use those programs as a UI for ML-backed algorithms. Nice.
Symbolic composition via scores/MIDI/etc
See ML for composition.
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