3 accepted papers
As an important learning paradigm for signal processing and pattern recognition, unsupervised domain adaptation (UDA), which deals with the learning bias induced by the changing data environments (i.e., domains), has achieved great success in real-world applications. Mainstream UDA methods commonly…
Optimal transport (OT) is an important methodology to measure distribution discrepancy, which has achieved promising performance in artificial intelligence applications, e.g., unsupervised domain adaptation. However, from the view of transportation, there are still limitations: 1) the local discrimi…
Unsupervised domain adaptation (UDA) is a representative problem in transfer learning, which aims to improve the classification performance on an unlabeled target domain by exploiting discriminant information from a labeled source domain. The optimal transport model has been used for UDA in the pers…