Catastrophic Forgetting

Catastrophic forgetting describes the phenomenon where artificial neural networks, upon learning new tasks, lose previously acquired knowledge. Current research focuses on mitigating this issue through various strategies, including parameter-efficient fine-tuning methods (like LoRA), generative model-based data replay, and novel optimization algorithms that constrain gradient updates or leverage hierarchical task structures. Addressing catastrophic forgetting is crucial for developing robust and adaptable AI systems capable of continuous learning in real-world applications, particularly in domains like medical imaging, robotics, and natural language processing where data streams are constantly evolving.