Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net論文筆記
論文主要貢獻有3點：1）IN學習到的是對形狀變換，比如顏色，風格，虛擬的或現實的不敏感的特徵，而BN是保留紋理相關的特徵；2）靈活，能夠用到很多的架構；3）在遷移學習的時候不需要目標領域的數據就能夠提高網絡的表現。 目的是：解決輸入圖片的外觀不變性。 方法是將IN和BN結合起來，IBN-Net。 結果是：IBN-Net能夠學習捕獲和消除外觀的變換，而保持學到的特徵的區別性。 結構特性：將IN和BN
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相關文章
- 1. 論文（5）Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
- 2. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net閱讀筆記
- 3. 論文筆記 understanding deep learning requires rethinking generalization
- 4. 論文筆記：All-at-once Optimization for Coupled Matrix and Tensor Factorizations
- 5. 論文閱讀筆記：Improving Generalization via Scalable Neighborhood Component Analysis
- 6. VITAL: VIsual Tracking via Adversarial Learning論文筆記
- 7. 【論文筆記】Deep Metric Learning via Facility Location
- 8. [CVPR 2019 論文筆記] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
- 9. 【論文筆記】Continual Relation Learning via Episodic Memory Activation and Reconsolidation
- 10. 論文閱讀筆記《Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders》