Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations.Recent approaches achieve this by directly mat...Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations.Recent approaches achieve this by directly matching the marginal distributions of these two domains.Most of them,however,ignore exploration of the dynamic trade-off between domain alignment and semantic discrimination learning,thus rendering them susceptible to the problems of negative transfer and outlier samples.To address these issues,we introduce the dynamic parameterized learning framework.First,by exploring domain-level semantic knowledge,the dynamic alignment parameter is proposed,to adaptively adjust the optimization steps of domain alignment and semantic discrimination learning.Besides,for obtaining semantic-discriminative and domain-invariant representations,we propose to align training trajectories on both source and target domains.Comprehensive experiments are conducted to validate the effectiveness of the proposed methods,and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.展开更多
基金Project supported by the National Natural Science Foundation of China (No.61932009)the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study,China。
文摘Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations.Recent approaches achieve this by directly matching the marginal distributions of these two domains.Most of them,however,ignore exploration of the dynamic trade-off between domain alignment and semantic discrimination learning,thus rendering them susceptible to the problems of negative transfer and outlier samples.To address these issues,we introduce the dynamic parameterized learning framework.First,by exploring domain-level semantic knowledge,the dynamic alignment parameter is proposed,to adaptively adjust the optimization steps of domain alignment and semantic discrimination learning.Besides,for obtaining semantic-discriminative and domain-invariant representations,we propose to align training trajectories on both source and target domains.Comprehensive experiments are conducted to validate the effectiveness of the proposed methods,and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.
