Triangle Generative Adversarial Networks
一、∆ -GAN理论 1. 引入 以成对数据集为训练集进行训练的GAN有很多，其中包括DualGAN、DiscoGAN、CycleGAN、Bidirectional GAN、TripleGAN等，文中提到由于DualGAN等是完全无监督的，导致一些不可识别的问题产生，所以应提供适当的监督，来匹配跨域的联合分布，因而提出了Triangle GAN。 ∆ -GAN与其他相似GAN的比较： (1) ∆
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相关文章
- 1. Graphical Generative Adversarial Networks
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