Continual Learner

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 developing novel regularization techniques, exploring the role of model architecture (including MLPs and Vision Transformers), and leveraging generative models and large language models to improve knowledge retention and transfer. These advancements are crucial for building more robust and adaptable AI systems, with applications ranging from autonomous driving and robotics to medical image analysis and personalized education.