Generalized Pareto GAN: Generating Extremes of Distributions

Extreme events are ubiquitous in various domains, including finance, meteorology, and network analysis. Nevertheless, modern deep learning techniques exhibit limitations in capturing extreme samples, treating them as outliers. In this paper, we propose Generalized Pareto Generative Adversarial Network (GPGAN), a novel generative model for tackling the generation of extreme values in distributions. Our methodology specifically entails the direct modeling of the multi-dimensional Generalized Pareto Distribution (GPD) using deep generative neural networks. Compared to prior work, GPGAN excels at faithfully representing the dependence structure exhibited by extreme values. To ensure effective generation, we employ an adaptive Generalized Pareto parameter strategy. This strategy dynamically selects appropriate GPD parameters for each generation step, enabling compatibility with user-defined extremeness functions within the multi-dimensional GPD framework. Extensive evaluations on a benchmark precipitation dataset demonstrate the effectiveness of our model.