Capacity Loss

Capacity loss, the degradation of a system's ability to learn or perform a task over time, is a central challenge across diverse fields, from machine learning to battery technology. Current research focuses on understanding and mitigating capacity loss in various contexts, employing techniques like regularization in reinforcement learning, improved model architectures (e.g., transformers, graph neural networks) to enhance robustness and scalability, and data-driven approaches for accurate prediction and analysis. Addressing capacity loss is crucial for improving the performance and reliability of numerous systems, ranging from autonomous vehicles and medical image generation to federated learning and battery lifespan prediction.