BEGAN: Boundary Equilibrium Generative Adversarial Networks閱讀筆記
BEGAN: Boundary Equilibrium Generative Adversarial Networks閱讀筆記 摘要 我們提出了一種新的用於促成訓練時生成器和判別器實現均衡（Equilibrium）的方法，以及一個配套的 loss，這個 loss 由 Wasserstein distance 衍生而來，Wasserstein distance 則是訓練基於自編碼器的生成對抗網絡（G
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相關文章
- 1. Generative Adversarial Nets[BEGAN]
- 2. 【閱讀筆記】Generative Adversarial Nets
- 3. Generative Adversarial Networks: An Overview文獻閱讀筆記
- 4. Conditional Generative Adversarial Networks（CGAN）筆記
- 5. Generating Adversarial Examples with Adversarial Networks閱讀筆記
- 6. BEGAN論文閱讀
- 7. Generative Adversarial Nets (GAN) 閱讀筆記
- 8. Generative Adversarial Nets 論文閱讀筆記
- 9. Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks閱讀筆記
- 10. DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks閱讀筆記