Lei Hua

DACL-IDA: a dynamic alignment and compactness learning framework for imbalanced domain adaptation in bearing fault diagnosis

This work proposes DACL-IDA (Dynamic alignment and compactness learning for imbalanced domain adaptation), built on an alignment-scheduling principle rather than a new alignment loss: global adversarial alignment is delayed until classification warmup and black-box shift estimation (BBSE) have stabilized, and is kept restrained thereafter.