Rehearsal Based Continual Learning

Rehearsal-based continual learning aims to enable artificial neural networks to learn from sequential data streams without forgetting previously acquired knowledge, a challenge known as catastrophic forgetting. Current research focuses on improving memory efficiency through techniques like compressed latent replays and coreset selection, as well as enhancing the effectiveness of rehearsal by employing generative models (e.g., using Dirichlet distributions), knowledge distillation, and regularization methods to manage inter-task interference and promote generalization. These advancements are significant for applications requiring lifelong learning from evolving data, such as personalized medicine, robotics, and continuously adapting dialogue systems.