  We design general-purpose algorithms for addressing fairness issues and mode
collapse in generative modeling. More precisely, to design fair algorithms for
as many sensitive variables as possible, including variables we might not be
aware of, we assume no prior knowledge of sensitive variables: our algorithms
use unsupervised fairness only, meaning no information related to the sensitive
variables is used for our fairness-improving methods. All images of faces (even
generated ones) have been removed to mitigate legal risks.
