Generative Models

We show the outputs of a generative model of sketches to human observers and record their facial expressions. Using only a small number of facial expression samples, we are able to tune the model to produce drawings that are significantly better rated by humans.

This demo deployed RL Tuner and other Magenta music generation models into an interactive interface in which users can collaborate creatively with a machine learning model. The interface supports call and response interaction, automatically generating an accompaniment to the user’s melody, or melody morphing: responding both with variations on the user’s melody and a bass accompaniment.

Generating music using traditional supervised sequence models suffers from known failure modes, including the inability to produce coherent global structure. Music is an interesting sequence generation problem, because musical compositions adhere to known rules. We impose these rules with a novel algorithm combining RL and supervised learning.