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Deep CORAL: Correlation Alignment for Deep Domain Adaptation
時間 2021-01-02
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本篇是遷移學習專欄介紹的第十六篇論文,由BU完成發表在ECCV2016上。 Abstract 深度神經網絡能夠從大量標記的輸入數據中學習強大的表示,但是它們不能很好地概括輸入分佈的變化。提出了一種域自適應算法來補償由於域移動而導致的性能下降。在本文中,我們解決了目標域未標記的情況,需要無監督的自適應。CORAL[1]是一種非常簡單的無監督域自適應方法,它用線性變換對源和目標分佈的二階統計量進行對齊
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相關文章
1.
Deep CORAL: Correlation Alignment for Deep Domain Adaptation(2016)
2.
域適應系列3:Minimal-entropy Correlation Alignment for Unsupervised Deep Domain Adaptation(ICLR 2018)
3.
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4.
[cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
5.
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M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
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SSPP-DAN: DEEP DOMAIN ADAPTATION NETWORK FOR FACE RECOGNITION WITH SINGLE SAMPLE PER PERSON
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