Stability Plasticity

Stability-plasticity in continual learning addresses the challenge of enabling artificial systems to learn new tasks without forgetting previously acquired knowledge. Current research focuses on balancing this trade-off through various techniques, including experience rehearsal, curriculum learning, and the development of novel model architectures like continual diffusion models and spiking neural networks, often incorporating methods such as weighted ensembling and knowledge distillation. This research is crucial for building robust and adaptable AI systems capable of operating in dynamic environments, with implications for applications ranging from robotics and autonomous vehicles to personalized medicine and lifelong learning agents.