Age Progression and Regression with Spatial Attention Modules（AAAI20）

时间 2020-12-30

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Method Problem Formulation 定义young face image为 I y \mathbf{I}_y Iy，对应的age为 α y \bm{\alpha}_y αy，是一个one-hot向量给定目标age α o \bm{\alpha}_o αo（要求 α o > α y \bm{\alpha}_o\gt\bm{\alpha}_y αo>αy），我们希望学习一

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1. The issus in Age Progression/Regression by Conditional Adversarial Autoencoder (CAAE)
2. SaGAN:Generative Adversarial Network with Spatial Attention for Face Attribute Editing
3. linear regression and logistic regression with pytorch
4. Paper Reading -- CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention
5. Learning Face Age Progression: A Pyramid Architecture of GANs
6. TextSpotter with Explicit Alignment and Attention
7. Regression with scikit-learn
8. Learning Continuous Face Age Progression: A Pyramid of GANs（CVPR18扩展）
9. Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
10. [AngularJS] Lazy Loading modules with ui-router and ocLazyLoad

>>更多相关文章<<