Transferrable Prototypical Networks for Unsupervised Domain Adaptation讀書筆記
這篇文章是cvpr2019的一篇域適應的文章，我覺得很不錯，和大家一起分享一下 在這篇文章裏，通過對每個類到原型的距離的重新映射，學習一個嵌入空間並執行分類。具體地說，提出了可轉移的原型網絡(TPN)來進行自適應，使源域和目標域的每個類的原型在嵌入空間上都很接近，並且原型在源和目標數據上分別預測的分數分佈也很簡單。從技術上講，TPN最初將每個目標示例與源域中最近的原型匹配，並將這個原型作爲示例的「
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相關文章
- 1. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 2. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 3. Geodesic flow kernel for unsupervised domain adaptation
- 4. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 5. [論文閱讀筆記@2019.6] Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 6. Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling讀書筆記
- 7. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 8. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 9. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)
- 10. Transferable Attention for Domain Adaptation閱讀筆記