Synthetic to real adaptation with generative correlation alignment network
3 contributions: We propose a Deep Generative Correlation Alignment Network(DGCAN) to synthesize CAD objects contour from the CAD-synthetic domain with natural textures from the real image domain. We
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相關文章
- 1. 論文閱讀Synthetic to Real Adaptation with Generative Correlation Alignment Networks
- 2. Deep CORAL: Correlation Alignment for Deep Domain Adaptation（2016）
- 3. Deep CORAL: Correlation Alignment for Deep Domain Adaptation
- 4. Real-Time monocular depth estimation using synthetic data with domain adaptation via IST
- 5. Domain Adaptation 與 Generative Adversarial Network
- 6. Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation
- 7. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 8. Finding Tiny Faces in the Wild with Generative Adversarial Network
- 9. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 10. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation