[CVPR 2019] Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
摘要: 論文提出域敏感的廣義零樣本學習方法。該方法包含三個結構，已見類分類器，未見類分類器，域判別器。 關鍵問題： 域判別器沒有任何未見類的訓練樣本。如何對未見類和已見類進行判別？ 已見類分類器和未見類分類器，如何對本域外的類進行分類？ 亮點： 通過交換信息來提升每個模塊的精度。
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相關文章
- 1. [CVPR 2019 論文筆記] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
- 2. [CVPR 2019] Gradient Matching Generative Networks for Zero-Shot Learning
- 3. Adaptive Reference Sample Smoothing(RSAF)
- 4. CVPR 2019 paper
- 5. 【Active Learning - 03】Adaptive Active Learning for Image Classification
- 6. Official Program for CVPR 2015
- 7. 【主動學習論文】Learning Loss for Active Learning, CVPR 2019
- 8. 論文閱讀：Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning（ICCV 2019）
- 9. [CVPR 2019] f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
- 10. Self-supervised Representation Learning from Videos for Facial Action Unit Detection(CVPR 2019)