Incremental Training

Incremental training, also known as continual learning, focuses on adapting machine learning models to new data without forgetting previously learned information. Current research emphasizes mitigating "catastrophic forgetting" through techniques like experience replay, knowledge distillation, and parameter isolation, often within specific architectures such as vision-language models or linear models designed for efficiency. This field is crucial for developing robust and adaptable AI systems capable of learning from continuous data streams in real-world applications, such as robotics, personalized recommendations, and anomaly detection, where retraining from scratch is impractical or impossible. The development of efficient and effective incremental training methods is driving progress in both theoretical understanding and practical deployment of AI.