Free Continual Learning

Free continual learning focuses on enabling AI models to learn sequentially from new data without needing to store or re-access past data, addressing the "catastrophic forgetting" problem where prior knowledge is lost. Current research emphasizes developing exemplar-free algorithms, often employing techniques like analytic solutions, prototype adaptation, and attention mechanisms within transformer and convolutional architectures to mitigate forgetting and handle data imbalance. This field is crucial for developing more environmentally sustainable and adaptable AI systems, particularly in resource-constrained or privacy-sensitive applications like autonomous driving and online learning.