Domain Separation Networks
本篇是遷移學習專欄介紹的第十九篇論文，發表在NIPS 2016上。Konstantinos Bousmali的工作。近期工作：Unsupervised Pixel–Level Domain Adaptation。 Abstract 大規模數據收集和註釋的成本常常使機器學習算法應用於新任務或數據集的成本高得令人望而卻步。規避這一成本的一種方法是在自動提供註釋的合成數據上訓練模型。儘管這些模型很有吸引
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相關文章
- 1. Domain-Adversarial Training of Neural Networks
- 2. Domain Adaptive Neural Networks for Object Recognition
- 3. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 4. Separate to Adapt: Open Set Domain Adaptation via Progressive Separation論文筆記
- 5. Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks論文筆記
- 6. speech separation
- 7. 【論文筆記 CVPR-2019】《Domain-Symmetric Networks for Adversarial Domain Adaptation》
- 8. (CVPR 2019)Domain-Symmetric Networks for Adversarial Domain Adaptation筆記
- 9. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 10. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking