[解讀] COEGAN Evaluating the Coevolution Effect in Generative Adversarial Networks
鏈接: https://arxiv.org/abs/1912.06172v1, https://arxiv.org/abs/1912.06180v1 本文提出一種協同進化的生成式對抗網絡(COEGAN), 將進化和協同訓練應用到 GAN 中, 該方法將能夠克服 GAN 訓練中遇到的梯度消失和模型崩潰問題, 能夠生成更有效的網絡結構. 試驗結果表明該方法訓練穩定並且表現優秀. 相關的工作 Neuro
相關文章
- 1. [解讀] ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
- 2. [解讀] Coupled Generative Adversarial Networks
- 3. Graphical Generative Adversarial Networks
- 4. Triangle Generative Adversarial Networks
- 5. Contrastive Generative Adversarial Networks
- 6. Self-Attention Generative Adversarial Networks 論文解讀
- 7. Generative Adversarial Networks overview（2）
- 8. Generative Adversarial Networks overview（1）
- 9. Generative Adversarial Networks overview（4）
- 10. Self-Attention Generative Adversarial Networks
- 更多相關文章...
- • SQL IN 操作符 - SQL 教程
- • Swift for-in 循環 - Swift 教程
- • JDK13 GA發佈：5大特性解讀
- • Scala 中文亂碼解決
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. [解讀] ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
- 2. [解讀] Coupled Generative Adversarial Networks
- 3. Graphical Generative Adversarial Networks
- 4. Triangle Generative Adversarial Networks
- 5. Contrastive Generative Adversarial Networks
- 6. Self-Attention Generative Adversarial Networks 論文解讀
- 7. Generative Adversarial Networks overview（2）
- 8. Generative Adversarial Networks overview（1）
- 9. Generative Adversarial Networks overview（4）
- 10. Self-Attention Generative Adversarial Networks