Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
introduction 作者認爲，domain adaption（域適應）方法旨在通過學習domain-invariant feature（域不變特徵）來橋接source domain和target domain，從而能夠在target domain沒有標籤的情況下，利用source domain所學到的分類器對target domain進行預測。 現在已經可以將domain adaption嵌
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相關文章
- 1. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)
- 2. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 3. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 4. 遷移學習-PixelDA-Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 5. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 6. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- 7. Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-i
- 8. Unsupervised Domain Adaptation by Backpropagation(2015)
- 9. Domain Adaptation總結（2017.9）
- 10. 【Transfer Learning】Adversarial Discriminative Domain Adaptation