Replay Based

Replay-based continual learning aims to mitigate catastrophic forgetting in artificial neural networks by rehearsing previously seen data during the training of new tasks. Current research focuses on improving memory efficiency through compression techniques and intelligent sample selection strategies, often incorporating uncertainty estimation or task similarity measures to guide the replay process. These advancements are crucial for enabling lifelong learning in resource-constrained environments and have significant implications for developing more robust and adaptable AI systems across various applications.