Compatible Learning

Compatible learning focuses on developing machine learning models whose outputs remain consistent and comparable even as the model is updated or adapted, addressing the challenge of maintaining compatibility across different model versions or training stages. Current research emphasizes techniques like feature alignment and partial backfilling to mitigate the computational cost of retraining entire datasets, exploring architectures such as flexible bidirectional transformers and leveraging in-storage processing for efficiency gains. This field is crucial for improving the scalability and robustness of large-scale machine learning systems, particularly in applications like recommendation systems and visual search where continuous model updates are necessary.