HybridRepair: towards annotation-efficient repair for deep learning models
Yu Li, Muxi Chen, Qiang Xu · 2022
A well-trained deep learning (DL) model often cannot achieve expected performance after deployment due to the mismatch between the distributions of the training data and the field data in the operational environment. Therefore, repairing DL models is critical, especially when deployed on increasingly larger tasks with shifted distributions.