AutoGAN: Neural Architecture Search for Generative Adversarial Networks阅读笔记
本文是ICCV2019的一篇文章，第一次将GAN利用网络架构搜索(NAS)来实现，非常有启发意义，遂写一篇阅读笔记。 网络架构搜索在图像分类等问题上均取得了一定的成功，这篇文章首次将NAS应用于GAN生成问题。我们定义了生成器结构变量的搜索空间，并使用一个RNN控制器来指导搜索，通过参数共享和动态重置来加速搜索过程。以inception得分作为奖励，引入多级搜索策略，以渐进的方式进行搜索。实验验证
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相关文章
- 1. A Style-Based Generator Architecture for Generative Adversarial Networks---阅读笔记
- 2. BEGAN: Boundary Equilibrium Generative Adversarial Networks阅读笔记
- 3. 论文阅读分享（一）AutoGAN-Distiller:Searching to Compress Generative Adversarial Networks
- 4. 【阅读笔记】Generative Adversarial Nets
- 5. AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks, ICML2020
- 6. 论文阅读笔记《Generative Adversarial Residual Pairwise Networks for One Shot Learning 》
- 7. 【论文阅读笔记】Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
- 8. NASE Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search 阅读笔记
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- 10. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization