Regularization Based Continual Learning

Regularization-based continual learning aims to enable machine learning models to learn new tasks sequentially without forgetting previously acquired knowledge, a crucial challenge in artificial intelligence. Current research focuses on improving existing regularization methods like Elastic Weight Consolidation (EWC) and exploring their application across various model architectures, including deep state-space models and vision transformers, often incorporating techniques like knowledge distillation. This field is significant because it addresses the limitations of traditional retraining approaches, paving the way for more robust and adaptable AI systems in real-world applications such as human activity recognition, autonomous driving, and video object segmentation.