Geodesic flow kernel for unsupervised domain adaptation
本篇是遷移學習專欄介紹的第八篇論文，2012年發表在CVPR，GFK作爲子空間變換方面是最爲經典的遷移學習方法，它建立在2011年發表在ICCV上的另一篇開創性質的文章（SGF）。 Abstract 在視覺識別的實際應用中，許多因素，如姿態、光照或圖像質量，都可能導致訓練分類器的源域與應用分類器的目標域之間的嚴重不匹配。因此，分類器在目標域中的性能通常很差。Domain Adaption技術旨在糾
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相關文章
- 1. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 2. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 3. [cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
- 4. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 5. Domain Adaptation for Object Recognition: An Unsupervised Approach
- 6. [CVPR 2018]Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 7. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 8. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
- 9. Unsupervised Domain Adaptation by Backpropagation(2015)
- 10. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）