Incremental Learning Framework

Incremental learning frameworks aim to enable machine learning models to continuously learn from new data streams without forgetting previously acquired knowledge, a crucial challenge in non-stationary environments. Current research focuses on mitigating "catastrophic forgetting" through techniques like data synthesis, neural unit dynamics manipulation, and knowledge distillation, often employing architectures such as masked autoencoders or adapting existing models for incremental learning. These advancements are significant for applications requiring continuous adaptation, such as medical image analysis, personalized recommendations, and real-time forecasting, where retraining from scratch is impractical or impossible.