Domain Generalization Research Based on Attention Mask
Deep learning performs well in distinguishing features,but when applied to unknown domains,trained models often experience performance degradation due to domain shift.In response to this situation,Domain Generalization(DG)learns transferable features from mul-tiple source domains and generalizes them to unknown target domains.Due to the bias of models trained in different fields towards the most prominent features,they often overlook general features related to the task,and transferable features are usually not the most prominent fea-tures in that field.Therefore,from this perspective,a regularization method based on attention masks is proposed to mask features,which gen-erates attention masks through the attention mask module to mask high weight features and improve the model's generalization performance.The experiment showed that the accuracy tested on three benchmark datasets increased by 2.6%,2.0%,and 4.2%compared to the baseline model,respectively,proving that this method can not only improve the performance of the model in unknown domains,but also reflect its uni-versality on domain generalization datasets.
万方数据
维普
