Task-Aware Multi-Expert Architecture For Lifelong Deep Learning

This article introduces a novel architecture for lifelong deep learning, focusing on task-aware multi-expert systems. The approach likely aims to improve performance and efficiency in scenarios where models continuously learn new tasks over time. The use of 'multi-expert' suggests a modular design, potentially allowing for specialization and knowledge transfer between tasks. The 'task-aware' aspect implies the system can identify and adapt to different tasks effectively. Further analysis would require examining the specific methods, datasets, and evaluation metrics used in the research.