Meta-Learning for Adaptive Model Training in Dynamic Environments

Abstract

In dynamic environments, where data distributions shift over time, maintaining model performance poses significant challenges. Traditional machine learning models struggle to adapt swiftly to these changes, often requiring retraining on new data. Meta-learning presents a promising approach to address this issue by enabling models to learn how to learn, thereby improving their adaptability and performance in evolving settings. This paper explores the application of meta-learning techniques for adaptive model training in dynamic environments, focusing on their effectiveness, challenges, and potential future directions.