DiDA: Iterative Boosting of Disentangled Synthesis and Domain Adaptation
2021 11th International Conference on Information Technology in Medicine and Education (ITME)(2021)
摘要
Unsupervised domain adaptation aims at learning a shared model for two related domains by leveraging supervision from a source domain to an unsupervised target domain. A number of effective domain adaptation approaches rely on the ability to extract domain-invariant latent factors which are common to both domains. Extracting latent commonality is also useful for disentanglement analysis. It enable...
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关键词
Adaptation models,Education,Benchmark testing,Feature extraction,Boosting,Data mining,Task analysis
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