Learning Oracle Attention for High-fidelity Face Completion
1. Motivation 人臉擁有豐富且微妙的紋理特徵，不同的人的這些特徵各不相同； 傳統方法不能處理具有複雜結構和大規模缺失的圖像，只能利用底層特徵； 一些基於深度學習的方法忽視了不精確的 Attention分數所帶來的影響。 2. Approach 2.1 Network Structure 整個網絡可以分別爲兩部分生成器和判別器，生成器部分採用U-Net的結構，判別器採用了PatchGAN
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相關文章
- 1. Learning disentangling and fusing networks for face completion under structured occlusions
- 2. Generative Face Completion
- 3. SaGAN:Generative Adversarial Network with Spatial Attention for Face Attribute Editing
- 4. Learning Residual Images for Face Attribute Manipulation
- 5. Similarity Metric Learning for Face Recognition2013
- 6. Geometry-Aware Face Completion and Editing
- 7. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 8. Face Paper：Face Attention Network: An Effective Face Detector for the Occluded Faces
- 9. 計算機視覺論文筆記四：Attention-Set based Metric Learning for Video Face Recognition
- 10. 《Multi-task Learning based Pre-trained Language Model for Code Completion》閱讀