COntinual Learning Framework

Continual learning frameworks aim to enable artificial intelligence models to learn new information sequentially without forgetting previously acquired knowledge, mirroring human learning. Current research focuses on addressing "catastrophic forgetting" through techniques like memory replay, regularization methods (e.g., elastic weight consolidation), and the development of novel architectures such as transformers and spiking neural networks tailored for continual learning. This field is crucial for developing robust and adaptable AI systems in resource-constrained environments and for applications like personalized medicine, autonomous systems, and online advertising where data streams are inherently non-stationary.