Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss

This research addresses problems with training implicit generative models (AI systems that learn to create new data similar to real data) by proposing the invariant statistical loss (ISL), which avoids unstable adversarial training by comparing the statistical ranks of real and generated samples. The authors improve ISL for two practical scenarios: using a Pareto distribution instead of Gaussian noise to better model extreme values in data, and introducing ISL-slicing to handle large multivariate datasets (data with many variables) by projecting onto random lower-dimensional subspaces.