Domain Adaptation for Object Recognition: An Unsupervised Approach
本篇是遷移學習專欄介紹的第十五篇論文，由馬里蘭大學帕克分校完成發表在ICCV2011上，引用量超600+的論文，算是domain adaptation發表較早的論文。 Abstract 將訓練在源域上的分類器用於識別來自新目標域的實例是最近受到關注的一個重要問題。在本文中，我們提出了在對象識別上下文中關於無監督域適應的首批研究之一，其中我們只標記了來自源域的數據(因此沒有跨域的對象類別之間的對應關
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相關文章
- 1. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 2. Geodesic flow kernel for unsupervised domain adaptation
- 3. A DIRT-T APPROACH TO UNSUPERVISED DOMAIN ADAPTATION
- 4. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 5. Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-ID
- 6. Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-id
- 7. [cvpr2017]Learning an Invariant Hilbert Space for Domain Adaptation
- 8. Domain Adaptive Neural Networks for Object Recognition
- 9. [cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
- 10. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation