行人屬性「Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Cl」
多任務深度學習網絡，一般是先設計網絡有一些共享層，然後有多個分支學習不同的任務。論文從一個較瘦的網絡開始，逐漸加粗。任務間進行選擇性共享，挖掘那些任務之間更相關。thin網絡使用SOMP初始化。 task-specific子網絡或分支：淺層特徵共享，深層特徵task-specific，類似屬性結構。計算量大，且受設計者主觀認識影響。 相關研究 多任務學習：一些方法認識到哪些任務之間可以共享。Hyp
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相關文章
- 1. Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classifi
- 2. 行人屬性「Person Attribute Recognition with a Jointly-trained Holistic CNN Model」
- 3. 行人屬性「Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios」
- 4. ARVE: Augmented Reality Applications in Vehicle to Edge Networks
- 5. In-depth exploration of attribute information for person re-identification
- 6. 行人屬性「Generative Adversarial Models for People Attribute Recognition in Surveillance」
- 7. Attribute-Recognition行人屬性識別資料
- 8. 行人屬性識別：Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute……
- 9. [Angular] Use Angular components in AngularJS applications with Angular Elements
- 10. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks