阅读小结：Improved Techniques for training GANS

时间 2020-07-25

标签阅读小结 improved techniques training gans 繁體版

原文原文链接

github地址：https://github.com/openai/improved-gan/git What:github 提出了对于GANs新的结构和训练过程。主要focus在两个应用：半监督学习和更好的图像产生。ide 对于G，不要求一个和test data和像的模型，也不要求不使用label。函数实验中，D在半监督分类任务上达到了state-of-art的结果。学习 G在MNIS

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1. Improved Techniques for Training GANs
2. 深度学习【40】Improved Techniques for Training GANs
3. 【阅读笔记】Improved Training of Wasserstein GANs
4. 学习笔记：Improved Techniques for Training GANs理解
5. Improved Techniques for Grid Mapping With RaoBlackwellized Particle Filters论文阅读（手写的）
6. PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
7. PROGRESSIVE GROWING OF GANs for IMPROVED QUALITY, STABILITY AND VARIATION
8. Reading Note: Progressive Growing of GANs for Improved Quality, Stability, and Variation
9. 【论文阅读】Triple GANs论文阅读
10. Unsupervised Data Augmentation For Consistency Training 论文阅读
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1. Improved Techniques for Training GANs
2. 深度学习【40】Improved Techniques for Training GANs
3. 【阅读笔记】Improved Training of Wasserstein GANs
4. 学习笔记：Improved Techniques for Training GANs理解
5. Improved Techniques for Grid Mapping With RaoBlackwellized Particle Filters论文阅读（手写的）
6. PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
7. PROGRESSIVE GROWING OF GANs for IMPROVED QUALITY, STABILITY AND VARIATION
8. Reading Note: Progressive Growing of GANs for Improved Quality, Stability, and Variation
9. 【论文阅读】Triple GANs论文阅读
10. Unsupervised Data Augmentation For Consistency Training 论文阅读

>>更多相关文章<<