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Jun 28, 2022 · We then propose a new Denoised Maximum Classifier Discrepancy (D-MCD) method for SFUDA to effectively address these two issues.
[PDF] Denoised Maximum Classifier Discrepancy for Source-Free ...
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Source-Free Unsupervised Domain Adaptation (SFUDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to the original labeled ...
Source-Free Unsupervised Domain Adaptation(SFUDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to the original ...
Benefited from the theoretical analysis, we then propose a new Denoised Maximum Classifier Discrepancy (D-MCD) for SFUDA to effectively address these two issues ...
This is a pytorch Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation.
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May 23, 2024 · Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation. T Chu, Y Liu, J Deng, W Li, L Duan. Proceedings of the AAAI conference ...
In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the ...
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Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation. Tong Chu, Yahao Liu, Jinhong Deng, Wen Li, Lixin Duan. [AAAI-22] Main ...