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• W2912771588 abstract "Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel approach that bridges the domain gap by projecting the source and target domains into a common association space through an unsupervised ``cross-grafted representation stacking'' (CGRS) mechanism. Specifically, we construct variational auto-encoders (VAE) for the two domains, and form bidirectional associations by cross-grafting the VAEs' decoder stacks. Furthermore, generative adversarial networks (GAN) are employed for label alignment (LA), mapping the target domain data to the known label space of the source domain. The overall adaptation process hence consists of three phases: feature representation learning by VAEs, association generation, and association label alignment by GANs. Experimental results demonstrate that our CGRS-LA approach outperforms the state-of-the-art on a number of unsupervised domain adaptation benchmarks." @default.
• W2912771588 created "2019-02-21" @default.
• W2912771588 creator A5030040261 @default.
• W2912771588 creator A5056969005 @default.
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• W2912771588 date "2019-02-17" @default.
• W2912771588 modified "2023-10-12" @default.
• W2912771588 title "Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks" @default.
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• W2912771588 doi "https://doi.org/10.48550/arxiv.1902.06328" @default.
• W2912771588 hasPublicationYear "2019" @default.
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