AttnGAN: Fine-Grained Text to Image Generationwith Attentional Generative Adversarial Networks 论文解读
attnGAN是CVPR 2018的一篇文章，我觉得写得很好，是十分值得一读的文章。文章引进了注意力（attention）机制。 Attentional Generative Adversarial Network(AttnGAN)能够生成细粒度（fine-grained）细节的的图片，与之前的text to image最好的 文章相比在CUB数据集上的inception score 提高了14.
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相关文章
- 1. AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
- 2. 论文阅读1《AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networ》
- 3. DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis 论文解读
- 4. Generative Adversarial Text to Image Synthesis 论文解读
- 5. text to image（二）:《Generative Adversarial Text to Image Synthesis》
- 6. Generative Adversarial Text to Image Synthesis
- 7. 《Generative Adversarial Text to Image Synthesis》阅读理解
- 8. Stack GAN:Text to Photo-realistic Image Synthesiswith Stacked Generative Adversarial Networks 论文解读
- 9. CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
- 10. 理解AttnGAN: Text-to-Image convertor