State of the Art Continual

Continual learning aims to enable machine learning models to learn new tasks sequentially without forgetting previously acquired knowledge, addressing the "catastrophic forgetting" problem. Current research focuses on improving the efficiency and effectiveness of continual learning algorithms, exploring various parameter-efficient fine-tuning (PEFT) techniques like prompt tuning and LoRA, and investigating the role of pre-trained models and rehearsal strategies. This field is crucial for developing more robust and adaptable AI systems capable of handling real-world data streams, with applications ranging from robotics and personalized medicine to resource-constrained embedded devices.