Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
目录 摘要 1、简介 2、相关工作 2.1、语义分割的域适配 2.2、风格迁移 2.3、纹理和形状 3、方法 3.1、程式化的GTA5和SYNTHIA 3.2、阶段1 3.3、阶段2 3.4、训练目标函数 4、实验 5、讨论 5.1、和基于CycleGAN的方法的比较 5.2、消融研究 5.3、鲁棒性测试 5.4、定性测试
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相关文章
- 1. Bidirectional Learning for Domain Adaptation of Semantic Segmentation详读
- 2. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 3. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 4. [cvpr2017]Learning an Invariant Hilbert Space for Domain Adaptation
- 5. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 6. 论文阅读 | A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
- 7. 极简笔记 Fully Convolutional Adaptation Networks for Semantic Segmentation
- 8. 论文翻译:Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
- 9. Review of Semantic Segmentation with Deep Learning
- 10. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation