Jun 7, 2021 · Domain adaptation aims to learn a discriminative or other types of predictor that reduces the shift between training and test distributions [5].
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Domain adaptation with feature and label adversarial networks. https://doi.org/10.1016/j.neucom.2021.01.062 ·. Journal: Neurocomputing, 2021, p. 294-301.
Adversarial Domain Adaptation: A technique to improve the performance of machine learning models when dealing with different data distributions between ...
Jul 2, 2021 · Domain Adaptation (DA) is a process for enhancing model training when there is a shift, often referred to as a co-variant shift or data shift, between input ...
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source ...
Apr 30, 2024 · In this study, we propose a vision transformer-based adversarial domain adaptation (VT-ADA) method. This method utilizes ViT as a feature extractor to learn ...
Aug 13, 2022 · Domain adaptation is learning an accurate model to transfer the knowledge extracted from source domain to the target domain with the presence of ...
Jul 1, 2022 · Domain Adaptation is a technique to improve the performance of a model on a target domain containing insufficient annotated data.
In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial. Network (CAN) through domain-collaborative and ...
Abstract. Recent advances in deep domain adaptation reveal that ad- versarial learning can be embedded into deep networks to learn transferable features ...