深度学习【47】Unsupervised Image-to-Image Translation with Generative Adversarial Networks
我们之前介绍了pix2pix和pix2pixHD都是属于有监督的图片到图片的转换，也就是其训练数据集要求每一张待转换图片必须有一张目标转换图像相对应。 今天我们来看看如何利用GAN做无监督的图像到图像的转换。既然是无监督的图像到图像转换，那么在训练数据集中肯定是没有成对的数据了。比如性别的转换，假设我有张奥巴马的图片，那么肯定不能找到一张女的奥巴马图片吧吧。所以这就叫无监督。还是以性别转换为例，我
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相关文章
- 1. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 3. 迁移学习-PixelDA-Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 4. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 5. 【翻译】Image-to-Image Translation with Conditional Adversarial Networks
- 6. IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING（Bingio-ICLR2017）
- 7. Generative Adversarial Nets[CycleGAN]
- 8. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 9. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation（CVPR18）
- 10. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》