Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning 論文筆記
摘要 如何利用少量的數據學習到一個generalizable 的分類器目前仍舊是小樣本學習的一個挑戰，基於這個出發點，作者把元學習(meta-learning)和深度度量學習和歸納推理想結合，通過探索每個任務中成對約束和正則化，我們將適應過程明確地公式化爲標準的半定規劃問題。作者針對每個任務設計了一個情節性的(episodic-wise)度量矩陣來將通用的，任務不可知的編碼空間轉換到一個可判別性高
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相關文章
- 1. 論文閱讀筆記《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 2. 論文閱讀：Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning（ICCV 2019）
- 3. 論文閱讀筆記《TADAM: Task dependent adaptive metric for improved few-shot learning》
- 4. TADAM: Task dependent adaptive metric for improved few-shot learning閱讀筆記
- 5. 【論文學習筆記】Transductive Unbiased Embedding for Zero-Shot Learning (2018_CVPR)
- 6. 【論文筆記】Deep Metric Learning via Facility Location
- 7. TADAM:Task dependent adaptive metric for improved few-shot learning
- 8. (Fewshot detection)Review:RepMet: Representative-based metric learning for few-shot detection
- 9. 論文閱讀筆記《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
- 10. 論文筆記《Deep Cosine Metric Learning for Person Re-Identification》