Deep Generative Model2--GAN(Generative Adversarial Network)-cont
Conditional Generation with GAN Text-to-Image 假设现在我们的目标是通过文字生成图片，如果使用传统的监督学习，我们把文字作为input，对应的图片作为output来训练NN，逻辑上似乎可行。但是效果却未必理想，原因在于，对与文字描述与图片，往往一段文字描述会对应多张图片，比如我们输入’行驶的火车’, 可能对应，正面驶来的火车，侧面驶来的火车等等，因此NN
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相关文章
- 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）译文
- 5. Seven paper：DCGAN（Deep convolution generative adversarial network）笔记
- 6. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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- 8. Generative Adversarial Nets[pix2pix]
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- 10. Triangle Generative Adversarial Networks