Self-Supervised Learning Enhanced Generative Models for Rare Event Detection

Abstract

Detecting rare events is a significant challenge in various fields, including finance, healthcare, cybersecurity, and environmental monitoring. Traditional
generative models, while powerful, often struggle with the scarcity of data associated with rare events, leading to suboptimal detection performance. This research introduces a novel approach: Self-Supervised Learning Enhanced Generative Models (SSLE-GMs), designed to improve the detection of rare events by leveraging self-supervised learning (SSL) techniques. The paper details the development and integration of self-supervised tasks into generative models, evaluates their performance in detecting rare events across different domains, and discusses the implications for real-world applications. Empirical results demonstrate that SSLE-GMs significantly enhance rare event detection accuracy, providing a robust tool for industries where the timely and accurate identification of such events is critical.