Model Upgrade

Model upgrading focuses on efficiently and effectively updating deployed machine learning models with new data or improved architectures, minimizing the substantial costs associated with retraining from scratch. Current research emphasizes mitigating "catastrophic forgetting" – where models lose performance on older data – through techniques like data rehearsal and methods that improve backward compatibility, such as gated fusion and bidirectional compatible training. These advancements are crucial for deploying models in dynamic environments, particularly in large-scale applications like image retrieval and natural language processing, where continuous improvement and seamless integration of expert feedback are paramount.