The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention recently.In DANNs,a discriminator is trained to discriminate the domain labels of features generated by a...The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention recently.In DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are aligned.As a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not considered.In this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain adaptation.DA2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple discriminators.Empirical results show that DA2NN can achieve better classification performance compared with the DANN methods.展开更多
基金The work was supported by the National Natural Science Foundation of China under Grant Nos.61876091 and 61772284the China Postdoctoral Science Foundation under Grant No.2019M651918the Open Foundation of Key Laboratory of Pattern Analysis and Machine Intelligence of Ministry of Industry and Information Technology of China.
文摘The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention recently.In DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are aligned.As a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not considered.In this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain adaptation.DA2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple discriminators.Empirical results show that DA2NN can achieve better classification performance compared with the DANN methods.
