解读 O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
解决问题 所有这些GAN变体都至少具有三个组成部分：生成器，鉴别器和编码器。 鉴别器和编码器具有相似的网络架构。 但是，随着训练过程的进行，大多数GAN中的生成器和编码器变得越来越完美，但鉴别器变得越来越原始。 换句话说，最优判别器在大多数情况下是原始且无用的。 我们能否在鉴别器和编码器之间共享权重？据我们所知，唯一成功做到这一点的是自省变分自动编码器（IntroVAEs，Huang等人（2018
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相关文章
- 1. [解读] Coupled Generative Adversarial Networks
- 2. Binary Generative Adversarial Networks for Image Retrieval
- 3. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 4. Generative Adversarial Networks for Hyperspectral Image Classification
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- 6. Triangle Generative Adversarial Networks
- 7. Contrastive Generative Adversarial Networks
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- 10. Generative Adversarial Networks overview（2）