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Extensive experiments and empirical analysis demonstrate the effectiveness of the feature disentanglement method on many real-world domain adaptation tasks.
This thesis explores how to learn effective transferable features by disentangling the deep features.
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Dec 22, 2020 · We aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.
Missing: adaptive | Show results with:adaptive
Typical domain adaptation techniques aim to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain in the same label ...
Missing: adaptive | Show results with:adaptive
Dec 25, 2017 · In this paper, a better learning network has been proposed by considering three tasks - domain adaptation, disentangled representation, and style transfer ...
An essential approach in unsupervised domain adaptation is to understand what the domain-invariant representation across the domains is and how to find it [ ...
Feature disentanglement based domain adaptation network for ...
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In this paper, a feature disentanglement based domain adaptation network (FDDAN) is proposed to disentangle and exclude domain-specific features and class- ...
In this paper, we propose the task of Domain-Agnostic Learn- ing (DAL): How to transfer knowledge from a labeled source domain to unlabeled data from ar-.
Missing: adaptive | Show results with:adaptive
Typical domain adaptation techniques aim to transfer label information from a label-rich source domain to a label-scarce target domain in the same label ...
Missing: adaptive | Show results with:adaptive
Sep 26, 2023 · We try to alleviate domain discrepancy in the region proposal network (RPN) by performing feature disentanglement.