解讀 O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
解決問題 所有這些GAN變體都至少具有三個組成部分：生成器，鑑別器和編碼器。 鑑別器和編碼器具有相似的網絡架構。 但是，隨着訓練過程的進行，大多數GAN中的生成器和編碼器變得越來越完美，但鑑別器變得越來越原始。 換句話說，最優判別器在大多數情況下是原始且無用的。 我們能否在鑑別器和編碼器之間共享權重？據我們所知，唯一成功做到這一點的是自省變分自動編碼器（IntroVAEs，Huang等人（2018
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相關文章
- 1. [解讀] 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
- 5. Graphical Generative Adversarial Networks
- 6. Triangle Generative Adversarial Networks
- 7. Contrastive Generative Adversarial Networks
- 8. [解讀] ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
- 9. Self-Attention Generative Adversarial Networks 論文解讀
- 10. Generative Adversarial Networks overview（2）