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Often, even with sufficient data, domain differences can cause a shift or bias in data distribution, affecting model performance during testing. Domain adaptation methods, especially adversarial techniques, are effective solutions for these challenges. The goal is to learn a classifier for an unlabeled target dataset using a labeled source dataset, enhancing resistance to domain shifts. However, existing methods sometimes struggle with adapting the joint feature distribution across domains, resulting in negative transfer. To address this, we propose a method that forms class-specific clusters to prevent negative transfer. This method is encapsulated in an unsupervised adversarial domain adaptation framework based on a variational auto-encoder. Our structure is designed to enhance invariant and discriminative feature representation. We process source and target data through a VAE to establish a smooth latent representation. In our method, source and target data are fed into a variational auto-encoder, which produces a smooth latent representation. The feature extractor then plays an adversarial minimax game with the discriminator to learn domain-invariant features, while the feature extractor is shared between the reconstructed source and reconstructed target data. In addition, we proposed a second structure in which the domain discriminator part of the prior structure is eliminated to demonstrate the influence of the variational auto-encoder in domain adaptation. On numerous unsupervised domain adaptation benchmarks, our results indicate that our proposed model outperforms or is comparable to state-of-the-art outcomes.<\/jats:p>","DOI":"10.1007\/s10994-025-06760-x","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T22:06:44Z","timestamp":1743286004000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An unsupervised adversarial domain adaptation based on variational auto-encoder"],"prefix":"10.1007","volume":"114","author":[{"given":"Mahta","family":"Hassan Pour Zonoozi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vahid","family":"Seydi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmood","family":"Deypir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,26]]},"reference":[{"issue":"518","key":"6760_CR1","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","volume":"112","author":"D Blei","year":"2017","unstructured":"Blei, D., Kucukelbir, A., & McAuliffe, J. 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