Passive unsupervised domain adaptation method based on meta-enhanced contrast learning

• Main Authors: ZHU XIAOFENG, ZHAN MENGMENG, WU ZONGQIAN
• Format: Patent
• Language: Chinese; English
• Published: 12.03.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

The invention discloses a passive unsupervised domain adaptation method based on meta-enhanced contrast learning, and the method comprises the steps: constructing a teacher-student network composed of a teacher model and a student model, and setting a meta-feature enhancer between a teacher model encoder and a classifier, thereby forming a meta-enhanced contrast learning model; a contrast learning feature library comprising a positive sample feature library, a negative sample feature library and a spatial feature library is constructed and initialized, then a meta-enhanced contrast learning model is trained based on spatial contrast learning and semantic contrast learning, and a student model is extracted from the final meta learning model as a target domain model. According to the invention, the meta-enhancement contrast learning model is constructed based on the teacher-student network and the meta-feature enhancer, and training is carried out through space contrast learning and semantic contrast learning,