Multi-Agent Lifelong Implicit Neural Learning

Implicit neural representations (INRs) have emerged as powerful tools for the continuous representation of signals, finding applications in imaging, computer graphics, and signal compression. Additionally, decentralized multi-agent systems are crucial in various applications, frequently leading to enhanced reliability and efficiencies in computation and communication. In this letter, we explore using multi-agent Lifelong Learning (LL) systems for learning INRs. We propose a rigorous problem setup and evaluation plan to investigate the efficacy of such systems compared to single-agent and multi-task learning baselines. Our research, conducted across varied dimensions, demonstrates promising results, thereby contributing a novel perspective to the realm of continual learning.