Incremental Accuracy

Incremental accuracy research focuses on improving the efficiency and adaptability of machine learning models, particularly in scenarios with limited resources or evolving data streams. Current efforts concentrate on developing methods that enhance model accuracy progressively, either by adding computational resources (e.g., through modular network designs like SteppingNet) or by strategically incorporating new data while preserving knowledge from previous training phases (e.g., using parameter-efficient tuning within continual learning frameworks). These advancements are significant for deploying machine learning in resource-constrained environments and for creating more robust and adaptable AI systems capable of handling continuous learning tasks.