Unsupervised Adversarial Depth Estimation using Cycled Generative Networks（2018.7）
代碼和模型：https://github.com/andrea-pilzer/unsup-stereo-depthGAN 摘要 提出一個生成網絡學習預測一個標定過的立體相機中兩幀圖詳見的相關信息（視差圖）。結構中包含兩個生成子網絡以及用於重建視差圖的對抗學習並建立在一個循環中比如相互之間提供人爲約束和監督。 介紹 之前的工作只考慮了重建損失，但是都沒有使用對抗學習的方法來改進合成的圖像，
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相關文章
- 1. Enhancing Underwater Imagery using Generative Adversarial Networks
- 2. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 3. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 4. Generative Adversarial Nets[CycleGAN]
- 5. Triangle Generative Adversarial Networks
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- 7. Contrastive Generative Adversarial Networks
- 8. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 9. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 10. Generative Adversarial Networks overview（1）