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SSPP-DAN: DEEP DOMAIN ADAPTATION NETWORK FOR FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON
時間 2020-12-30
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論文相關內容 本文中域適應的方法及目的 摘要:在現實世界中,每個人使用一個樣本進行人臉識別(SSPP)是一項具有挑戰性的任務。如果捕捉畫廊圖像和探測集的條件完全不同,問題就會加劇。爲了從領域適應的角度解決這些問題,我們引入了一個SSPP領域適應網絡(SSPP- dan)。在提出的方法中,結合領域對抗性訓練和深度體系結構,實現了領域自適應、特徵提取和分類。然而,每個類一個訓練樣本的SSPP特性不足以
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相關文章
1.
Deep Learning Based Single Sample Per Person Face Recognition: A Survey
2.
Local Generic Representation for Face Recognition with Single Sample per Person (ACCV, 2014)
3.
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5.
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DeepID3: Face Recognition with Very Deep Neural Networks
7.
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10.
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