Deep Generative Model2--GAN(Generative Adversarial Network)-cont
Conditional Generation with GAN Text-to-Image 假設現在我們的目標是通過文字生成圖片，如果使用傳統的監督學習，我們把文字作爲input，對應的圖片作爲output來訓練NN，邏輯上似乎可行。但是效果卻未必理想，原因在於，對與文字描述與圖片，往往一段文字描述會對應多張圖片，比如我們輸入’行駛的火車’, 可能對應，正面駛來的火車，側面駛來的火車等等，因此NN
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相關文章
- 1. Generative Adversarial Nets[CycleGAN]
- 2. Generative Adversarial Nets_GAN_paperReading
- 3. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 4. Seven paper：DCGAN（Deep convolution generative adversarial network）譯文
- 5. Seven paper：DCGAN（Deep convolution generative adversarial network）筆記
- 6. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 7. Generative Adversarial Nets[BEGAN]
- 8. Generative Adversarial Nets[pix2pix]
- 9. Conditional Generative Adversarial Nets
- 10. Triangle Generative Adversarial Networks