Generative Adversarial Network 3 WGAN/ EBGAN
前提回顧 JS 散度 Jensen-Shannon Divergence 解決了兩個概率分佈的相似度，值0-1之間 但是如果P,Q離得很遠，完全沒有重疊的時候，KL散度值是沒有意義的，JS散度值是個常數，這就意味着這一點梯度爲0. JS divergence is not suitable in most case，PGandPdata are not overlapped 1.PGandPdat
相關文章
- 1. Wasserstein Generative Adversarial Nets (WGAN ) and CGAN
- 2. GAN（Generative Adversarial Network）的學習歷程
- 3. GAN--6 WGAN EBGAN
- 4. 【李宏毅2020 ML/DL】P79 Generative Adversarial Network | Tips for improving GAN
- 5. ASRWGAN: Wasserstein Generative Adversarial Network for Audio Super Resolution
- 6. [Style Transfer]Generative Adversarial Network in Medical Imaging: A Review
- 7. Generative Adversarial Nets[BEGAN]
- 8. Segan : Speech Enhancement Generative Adversarial Network
- 9. Deep Generative Model2--GAN(Generative Adversarial Network)-cont
- 10. Domain Adaptation 與 Generative Adversarial Network
- 更多相關文章...
- • MySQL INSERT：插入數據（添加數據） - MySQL教程
- • MySQL UPDATE：修改數據（更新數據） - MySQL教程
- • Flink 數據傳輸及反壓詳解
- • 算法總結-廣度優先算法
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. Wasserstein Generative Adversarial Nets (WGAN ) and CGAN
- 2. GAN（Generative Adversarial Network）的學習歷程
- 3. GAN--6 WGAN EBGAN
- 4. 【李宏毅2020 ML/DL】P79 Generative Adversarial Network | Tips for improving GAN
- 5. ASRWGAN: Wasserstein Generative Adversarial Network for Audio Super Resolution
- 6. [Style Transfer]Generative Adversarial Network in Medical Imaging: A Review
- 7. Generative Adversarial Nets[BEGAN]
- 8. Segan : Speech Enhancement Generative Adversarial Network
- 9. Deep Generative Model2--GAN(Generative Adversarial Network)-cont
- 10. Domain Adaptation 與 Generative Adversarial Network