Adversarial learning for semi-supervised semantic segmentation
GAN生成对抗网络：由两个子网络组成，generator和discriminator,在训练过程中，这两个子网络进行着最小最大值机制，generator用随机向量输出一个目标数据分布的样例，discriminator从目标样例中区分出生成器生成的样例。generator通过后向传播混淆discriminator，依此generator生成与目标样例相似的样例。 这篇论文中，将generator换成
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相关文章
- 1. 论文：Adversarial Learning for Semi-Supervised Semantic Segmentation
- 2. 《Learning Dynamic Routing for Semantic Segmentation》
- 3. Learning Deconvolution Network for Semantic Segmentation
- 4. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 5. Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018
- 6. Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
- 7. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 8. Learning to Adapt Structured Output Space for Semantic Segmentation
- 9. Adversarial learning with multi-scale loss for skin lesion segmentation
- 10. [ACL2017]Adversarial Multi-Criteria Learning for Chinese Word Segmentation