论文学习:Learning to Generate Time-Lapse Videos Using Multi-StageDynamic Generative Adversarial Networks
Welcome To My Blog 这篇论文收录于KDD2018,有关视频生成的,论文有个项目主页,题目翻译过来大致是:使用多阶段动态生成对抗网络学习生成time-lapse(延时)视频. 多阶段具体来说是两阶段, 1. 第一阶段(Base-Net): 注重每一帧内容的真实性 2. 第二阶段(Refine-Net): 注重帧与帧之间物体的运动 下图是模型整体框架,叫做MD-GAN MD-GAN由
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相关文章
- 1. Enhancing Underwater Imagery using Generative Adversarial Networks
- 2. [semantic segmentation] using Adversarial Networks 论文学习
- 3. Generative adversarial networks to segment skin lesions
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- 6. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 7. Unsupervised Adversarial Depth Estimation using Cycled Generative Networks（2018.7）
- 8. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 9. Generative Adversarial Nets[CycleGAN]
- 10. 【论文总结】Enhancing Underwater Imagery using Generative Adversarial Networks