A showcase with creative machine learning experiments
This database* is an ongoing project to aggregate tools and resources for artists, engineers, curators & researchers interested in incorporating machine learning (ML) and other forms of artificial intelligence (AI) into their practice. Resources in the database come from our partners and network; tools cover a broad spectrum of possibilities presented by the current advances in ML like enabling users to generate images from their own data, create interactive artworks, draft texts or recognise objects. Most of the tools require some coding skills, however, we’ve noted ones that don’t. Beginners are encouraged to turn to RunwayML or entries tagged as courses.
*This database isn’t comprehensive—it's a growing collection of research commissioned & collected by the Creative AI Lab. The latest tools were selected by Luba Elliott. Check back for new entries.
Via : https://docs.google.com/document/d/1TkusCE5mS4tuTYoBwaTV4aJKdSVsf9jFKsGJCx8M05c/edit
https://github.com/msieg/deep-music-visualizer
https://www.instagram.com/deep_music_visualizer/
https://www.youtube.com/watch?v=L7R-yBZ5QYc
Want to make a deep music video? Wrap your mind around BigGAN. Developed at Google by Brock et al. (2018)¹, BigGAN is a recent chapter in a brief history of generative adversarial networks (GANs). GANs are AI models trained by two competing neural networks: a generator creates new images based on statistical patterns learned from a set of example images, and a discriminator tries to classify the images as real or fake. By training the generator to fool the discriminator, GANs learn to create realistic images.
We call them "seeds". Each seed is a machine learning example you can start playing with. Explore, learn and grow them into whatever you like.
