Parallel-data-free voice conver- sion using cycle-consistent adversarial networks
會議：EUSIPCO 2018 單位：日本NTT 1. abstract 本文實現不需要平行數據，不需要額外的數據，模型，對齊方式實現的高質量，通用的語音轉換。用帶門控CNN的CycleGAN-VC實現，用adversarial 和cycle consistent loss同時進行正向和反向的映射，從而在不匹配的數據中找到最佳的僞對（psedu paired)。 2. introduction a
相關文章
- 1. Voice Conversion using Convolutional Neural Networks 翻譯
- 2. Enhancing Underwater Imagery using Generative Adversarial Networks
- 3. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 4. Unsupervised Adversarial Depth Estimation using Cycled Generative Networks（2018.7）
- 5. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 6. CVPR：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 7. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 8. CycleGAN:Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 9. 《Semantic Segmentation using Adversarial Networks》論文筆記
- 10. [semantic segmentation] using Adversarial Networks 論文學習
- 更多相關文章...
- • ASP.NET MVC - 控制器 - ASP.NET 教程
- • C# 命名空間（Namespace） - C#教程
- • RxJava操作符（六）Utility
- • C# 中 foreach 遍歷的用法
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. Voice Conversion using Convolutional Neural Networks 翻譯
- 2. Enhancing Underwater Imagery using Generative Adversarial Networks
- 3. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 4. Unsupervised Adversarial Depth Estimation using Cycled Generative Networks（2018.7）
- 5. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 6. CVPR：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 7. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 8. CycleGAN:Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 9. 《Semantic Segmentation using Adversarial Networks》論文筆記
- 10. [semantic segmentation] using Adversarial Networks 論文學習