Rehearsal Based

Rehearsal-based continual learning aims to enable artificial intelligence models to learn new tasks sequentially without forgetting previously acquired knowledge, a challenge known as catastrophic forgetting. Current research focuses on improving rehearsal methods by enhancing data selection strategies for replay buffers (e.g., prioritizing diverse and representative samples), developing data-free generative replay techniques to circumvent the need for data storage, and integrating rehearsal with other approaches like prompt engineering and regularization to improve performance and efficiency. These advancements are significant because they address a critical limitation of current AI systems, paving the way for more robust and adaptable models in various applications, including image and speech recognition, and natural language processing.