Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks論文閱讀中的問題和思考 1.有監督的串行GAN網絡結構 在圖示的網絡結果中，G1的輸入是真實圖像編碼後的結果，而非直接輸入真實圖像。這個應該是VAE(Variational Autoencoder,
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相關文章
- 1. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 2. Medical Image Synthesis with Context-Aware Generative Adversarial Networks （簡介）
- 3. Generative Adversarial Text to Image Synthesis
- 4. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 5. 論文閱讀——Deformable Medical Image Registration Using Generative Adversarial Networks
- 6. text to image（二）:《Generative Adversarial Text to Image Synthesis》
- 7. Generative Adversarial Text to Image Synthesis 論文解讀
- 8. Enhancing Underwater Imagery using Generative Adversarial Networks
- 9. Binary Generative Adversarial Networks for Image Retrieval
- 10. Generative Adversarial Networks for Hyperspectral Image Classification