讀論文：LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
1 前言 傳統算法做美妝遷移的時候，如下圖所示，把 A* 的美妝遷移到 B 上面。但是這樣會有一些問題： A 和 A* 難以同時獲得 A 和 B 要做對齊工作 其他 而 LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup 這篇文章，解決了上述的一些缺點，其效果特色在於：
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相關文章
- 1. LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
- 2. LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup（ICCV19）
- 3. [MM18]BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network
- 4. 【論文閱讀】Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
- 5. 論文閱讀 Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network
- 6. Adversarial Network Embedding論文解讀
- 7. 論文閱讀——Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks
- 8. Explaining and Harnessing Adversarial Examples 論文閱讀
- 9. 【論文快讀】Explaining and Harnessing Adversarial Examples（2015）
- 10. 論文閱讀（6）：Deep Clustering and Block Hashing Network for Face Image Retrieval