SSPP-DAN: DEEP DOMAIN ADAPTATION NETWORK FOR FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON
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. Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-i
- 4. 【論文閱讀】Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
- 5. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- 6. DeepID3: Face Recognition with Very Deep Neural Networks
- 7. Deep visual domain adaptation: A survey
- 8. Deep CORAL: Correlation Alignment for Deep Domain Adaptation（2016）
- 9. 【論文閱讀】Universal Domain Adaptation
- 10. Deep CORAL: Correlation Alignment for Deep Domain Adaptation