深度學習【47】Unsupervised Image-to-Image Translation with Generative Adversarial Networks
我們之前介紹了pix2pix和pix2pixHD都是屬於有監督的圖片到圖片的轉換，也就是其訓練數據集要求每一張待轉換圖片必須有一張目標轉換圖像相對應。 今天我們來看看如何利用GAN做無監督的圖像到圖像的轉換。既然是無監督的圖像到圖像轉換，那麼在訓練數據集中肯定是沒有成對的數據了。比如性別的轉換，假設我有張奧巴馬的圖片，那麼肯定不能找到一張女的奧巴馬圖片吧吧。所以這就叫無監督。還是以性別轉換爲例，我
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相關文章
- 1. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 3. 遷移學習-PixelDA-Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 4. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
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- 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. 論文譯文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》