Transfer Weight Based Conditional Adversarial Domain Adaptation

Abstract

Considering the failure of the Conditional adversarial Domain AdaptatioN(CDAN) to fully utilize the sample transferability, which still struggle with some hard-to-transfer source samples disturbed the distribution of the target domain samples, a Transfer Weight based Conditional adversarial Domain AdaptatioN(TW-CDAN) is proposed. Firstly, the discriminant results in the domain discriminant model as the main factor are employed to measure the transfer performance. Then the weight is applied to class loss and minimum entropy loss. It is for eliminating the influence of hard-to-transfer samples of the model. Finally, experiments are carried out using the six domain adaptation tasks of the Office-31 dataset and the 12 domain adaptation tasks of the Office-Home dataset. The proposed method improves the 14 domain adaptation tasks and increases the average accuracy by 1.4% and 3.1% respectively.