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• W4289277558 startingPage "108229" @default.
• W4289277558 abstract "The research involved Unsupervised Domain Adaptation (UDA) has huge emerged. However, almost of them assume that the domain distributions could be transformed into a shared representation space, which does not always hold, making domain-invariant representations challenging to generate directly on the original domains. We propose a novel UDA algorithm introducing a bidirectional generation and middle domains alignment (BGMA) that uses the idea of transmitting middle domains to reduce a cross-domain gap. To explore the middle domains (i.e., fake source/target domain), we embed supervised knowledge in the constructed task network to train a bidirectional generator. Besides, we replace the source and target domains with the generated middle domains, and we reassign the relative influence of each sample to the clusters’ centers. Then we specifically design an algorithm to optimize our method with generalization bound analysis and demonstrate its effectiveness through a comprehensive evaluation." @default.
• W4289277558 created "2022-08-01" @default.
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• W4289277558 date "2022-09-01" @default.
• W4289277558 modified "2023-10-08" @default.
• W4289277558 title "Unsupervised Domain Adaptation via bidirectional generation and middle domains alignment" @default.
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• W4289277558 doi "https://doi.org/10.1016/j.compeleceng.2022.108229" @default.
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