Model Retraining

Model retraining focuses on improving the performance and adaptability of machine learning models after initial training, addressing issues like data drift, concept drift, and the need for enhanced accuracy or efficiency. Current research explores various techniques, including incorporating new data, adjusting model architectures (e.g., pruning, knowledge distillation), and optimizing retraining strategies (e.g., sample-level selection, multi-objective optimization) across diverse model types such as decision trees, large language models, and deep neural networks. These advancements are crucial for maintaining the reliability and effectiveness of deployed models in dynamic real-world applications, impacting fields ranging from medical imaging and robotics to natural language processing and aerospace.