Learning Face Age Progression: A Pyramid Architecture of GANs
前言 作爲IP模式識別的CNN初始模型是作爲單純判別式-模式識別存在的，並以此爲基本模型擴展到各個方向。基本功能爲圖像判別模型，此後基於Loc+CNN的檢測模型-分離式、end2end、以及MaskCNN模型，而後出現基於CNN的預測模型-AcGans。 CNN作爲一個基本判別式模型簡化爲數學模型依然爲一個函數映射f(x)->y; 基於CNN的檢測模型數學模型爲 L(
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相關文章
- 1. Learning Face Age Progression: A Pyramid Architecture of GANs
- 2. Learning Face Age Progression: A Pyramid Architecture of GANs筆記
- 3. Learning Continuous Face Age Progression: A Pyramid of GANs（CVPR18擴展）
- 4. Age Progression and Regression with Spatial Attention Modules（AAAI20）
- 5. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
- 6. Architecture of a DRM driver
- 7. The issus in Age Progression/Regression by Conditional Adversarial Autoencoder (CAAE)
- 8. A Discriminative Feature Learning Approach for Deep Face Recognition
- 9. Deep learning approach for facial age classification: a survey of the state‑of‑the‑art 論文閱讀
- 10. Neuroevolution: A different kind of deep learning