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Learning Oracle Attention for High-fidelity Face Completion
時間 2020-12-30
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計算機視覺
深度學習
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Oracle
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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
>>更多相關文章<<