[survey 2019] A Survey of Zero-Shot Learning: Settings, Methods, and Applications
[blog] [paper]web 這篇文章主要貢獻以下：網絡 對零樣本學習分類進行了層次劃分 對零樣本學習設定進行了形式化分類和定義 對零樣本學習語義空間進行了分類 固然，還有很重要的一點是，文章對將來方向的分析。機器學習 語義空間分類 v.s. 零樣本學習方法分類 1. Introduction 爲何零樣本學習？ 緣由有二：svg 但現實場景中，許多的任務須要對從未見過的樣本進行分類 不少任務
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相關文章
- 1. 綜述論文「A Survey of Zero-Shot Learning: Settings, Methods, and Applications」
- 2. A Survey of Zero-Shot Learning: Settings, Methods, and Applications [reading notes]
- 3. A Comprehensive Survey of Graph Embedding:Problems, Techniques and Applications
- 4. Survey and experimental study on metric learning methods
- 5. A Survey of Few-Shot Learing | Data
- 6. Image registration methods: a survey
- 7. Knowledge Graph Embedding: A Survey of Approaches and Applications 摘要
- 8. 【論文筆記】Graph Summarization Methods and Applications- A Survey
- 9. A Survey on Few-Shot Learning | Introduction and Overview
- 10. A Survey of Network Embedding