Geodesic flow kernel for unsupervised domain adaptation
本篇是迁移学习专栏介绍的第八篇论文，2012年发表在CVPR，GFK作为子空间变换方面是最为经典的迁移学习方法，它建立在2011年发表在ICCV上的另一篇开创性质的文章（SGF）。 Abstract 在视觉识别的实际应用中，许多因素，如姿态、光照或图像质量，都可能导致训练分类器的源域与应用分类器的目标域之间的严重不匹配。因此，分类器在目标域中的性能通常很差。Domain Adaption技术旨在纠
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相关文章
- 1. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 2. Re-weighted adversarial adaptation network for unsupervised domain adaptation
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- 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）