Continual Learning

Continual learning aims to enable artificial intelligence models to learn new tasks sequentially without forgetting previously acquired knowledge, mirroring human learning capabilities. Current research focuses on mitigating "catastrophic forgetting" through techniques like experience replay, regularization, parameter isolation, and the use of parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA) and prompt tuning within various architectures including transformers and convolutional neural networks. This field is crucial for developing robust and adaptable AI systems across diverse applications, from autonomous driving and robotics to medical image analysis and personalized education, where continuous adaptation to new data is essential.