Continual Learning Algorithm

Continual learning algorithms aim to enable artificial intelligence models to learn from a continuous stream of data without forgetting previously acquired knowledge, a phenomenon known as catastrophic forgetting. Current research focuses on improving the efficiency and robustness of these algorithms, exploring various approaches such as replay buffers, regularization techniques, and parameter isolation methods, often applied to pre-trained models like Vision Transformers. This field is crucial for developing more adaptable and environmentally sustainable AI systems, addressing challenges in real-world applications where data is constantly evolving and resource constraints are significant.