CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
text2image相比單一的圖像生成任務來說，其目標不僅是生成與目標分佈一致的圖像，而且還要使得生成的圖像符合輸入描述，也就是說相比單純的圖像生成任務來說，text2image是一個可以通過輸入控制輸出的任務。本文建立在大家已經對這個領域有了充分了解的基礎上介紹最近一篇基於GAN的生成方法CPGAN，這篇論文采用全階段解析的方式生成符合文本描述的圖像，其基本架構如下： 其中的全階段解析包含三個方
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相關文章
- 1. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 2. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 3. Generative Adversarial Text to Image Synthesis
- 4. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 5. Binary Generative Adversarial Networks for Image Retrieval
- 6. Generative Adversarial Networks for Hyperspectral Image Classification
- 7. DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis 論文解讀
- 8. [TMI2019-07]Ea-GANs: Edge-Aware Generative Adversarial Networks for CrossModality MR Image Synthesis
- 9. Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis
- 10. Graphical Generative Adversarial Networks