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.
Papers
When Life gives you LLMs, make LLM-ADE: Large Language Models with Adaptive Data Engineering
Stephen Choi, William Gazeley
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting
Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen
FedMeS: Personalized Federated Continual Learning Leveraging Local Memory
Jin Xie, Chenqing Zhu, Songze Li