# Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events

arXiv: https://arxiv.org/abs/2506.06380 · HTML: https://mlsanity.com/paper/2506.06380/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Model collapse from synthetic data, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 17 of its 72 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Model collapse from synthetic data](https://mlsanity.com/synth/model-collapse-from-synthetic-data/)

## Builds on (most cited first, 17 of 17)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [VAE with a VampPrior](https://mlsanity.com/paper/1705.07120/) (2017)
- [ExGAN: Adversarial Generation of Extreme Samples](https://mlsanity.com/paper/2009.08454/) (2020)
- [Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions](https://mlsanity.com/paper/2101.09113/) (2021)
- [DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models](https://mlsanity.com/paper/2409.11601/) (2024)
- [DeepClimGAN: A High-Resolution Climate Data Generator](https://mlsanity.com/paper/2011.11705/) (2020)
- [Generative modeling of spatio-temporal weather patterns with extreme event conditioning](https://mlsanity.com/paper/2104.12469/) (2021)
- [Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision](https://mlsanity.com/paper/2412.15222/) (2024)
- [Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network](https://mlsanity.com/paper/2302.14164/) (2023)
- [Combining deep generative models with extreme value theory for synthetic hazard simulation: a multivariate and spatially coherent approach](https://mlsanity.com/paper/2311.18521/) (2023)
- [HI-GAN: Hierarchical Inpainting GAN with Auxiliary Inputs for Combined RGB and Depth Inpainting](https://mlsanity.com/paper/2402.10334/) (2024)
- [Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)](https://mlsanity.com/paper/2411.15519/) (2024)

## Built on by (most cited first, 5 of 5)

- [The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety](https://mlsanity.com/paper/2601.06116/) (2026)
- [E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation](https://mlsanity.com/paper/2606.01634/) (2026)
- [TailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement](https://mlsanity.com/paper/2608.11951/) (2026)
- [Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative](https://mlsanity.com/paper/2609.12113/) (2026)
- [Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation](https://mlsanity.com/paper/2610.17407/) (2026)

## Abstract (arXiv)

Extreme events, such as market crashes, natural disasters, and pandemics, are rare but catastrophic, often triggering cascading failures across interconnected systems. Accurate prediction and early warning can help minimize losses and improve preparedness. While data-driven methods offer powerful capabilities for extreme event modeling, they require abundant training data, yet extreme event data is inherently scarce, creating a fundamental challenge. Synthetic data generation has emerged as a powerful solution. However, existing surveys focus on general data with privacy preservation emphasis, rather than extreme events' unique performance requirements. This survey provides the first overview of synthetic data generation for extreme events. We systematically review generative modeling techniques and large language models, particularly those enhanced by statistical theory as well as specialized training and sampling mechanisms to capture heavy-tailed distributions. We summarize benchmark datasets and introduce a tailored evaluation framework covering statistical, dependence, visual, and task-oriented metrics. A central contribution is our in-depth analysis of each metric's applicability in extremeness and domain-specific adaptations, providing actionable guidance for model evaluation in extreme settings. We categorize key application domains and identify underexplored areas like behavioral finance, wildfires, earthquakes, windstorms, and infectious outbreaks. Finally, we outline open challenges, providing a structured foundation for advancing synthetic rare-event research.