Self-Attention Generative Adversarial Networks 论文解读
论文地址：https://arxiv.org/abs/1805.08318 Ian goodfellow 2018年 新作，在GAN生成中加入了attention的机制，同时将SNgan的思想引入到生成器当中。 传统gan的问题 使用小的卷积核很难发现图像中的依赖关系 使用大的卷积核就丧失了卷积网络参数与计算的效率 attention层详解 就是在前一层的feature maps 上加入atte
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相关文章
- 1. [解读] Coupled Generative Adversarial Networks
- 2. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 3. 论文阅读——《Generative Adversarial Nets》
- 4. Generative Adversarial Text to Image Synthesis 论文解读
- 5. SAGAN(Self-Attention Generative Adversarial Networks)论文解读附自己的理解
- 6. Triangle Generative Adversarial Networks
- 7. Contrastive Generative Adversarial Networks
- 8. [论文笔记]Self-Attention Generative Adversarial Networks
- 9. SAGAN(Self-Attention Generative Adversarial Networks)论文笔记
- 10. SA-GAN - Self-Attention Generative Adversarial Networks 论文解读（附代码）