【論文筆記】Recent Advances on Spectral-Spatial HSIC：An Overview and New Guidance
1.貢獻點 第一，提出「Spatial Dependancy Systems」的概念，即像元/標籤實體和具有覆蓋範圍和權重兩大特性的鄰域的聚合體，並將「Spatial Dependancy Systems」分爲fixed、adaptive、global systems（根據定義鄰域的方式）或single-dependancy、bilayer-dependancy、multiple-depend
相關文章
- 1. 論文閱讀筆記：Recent Advances and Challenges in Task-oriented Dialog Systems
- 2. Deep Multimodal Learning A survey on recent advances and trends讀書筆記
- 3. 《Recent Advances in Convolutional Neural Networks》(一)
- 4. Vehicle-to-Vehicle Channel Modeling and Measurements: Recent Advances and Future Challenges
- 5. Recent Progress in Road and Lane Detection - A survey論文筆記（一）
- 6. A Survey on Few-Shot Learning | Introduction and Overview
- 7. Image Processing and Computer Vision_Review：A survey of recent advances in visual feature detection—...
- 8. 深度學習的最新進展：綜述 Recent Advances in Deep Learning: An Overview
- 9. HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs--論文筆記
- 10. Call for Papers IEEE/ACM International Conference on Advances in Social Network Analysis and Mining
- 更多相關文章...
- • ASP.NET Razor - 標記 - ASP.NET 教程
- • CAP理論是什麼？ - NoSQL教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • RxJava操作符（七）Conditional and Boolean
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 論文閱讀筆記：Recent Advances and Challenges in Task-oriented Dialog Systems
- 2. Deep Multimodal Learning A survey on recent advances and trends讀書筆記
- 3. 《Recent Advances in Convolutional Neural Networks》(一)
- 4. Vehicle-to-Vehicle Channel Modeling and Measurements: Recent Advances and Future Challenges
- 5. Recent Progress in Road and Lane Detection - A survey論文筆記（一）
- 6. A Survey on Few-Shot Learning | Introduction and Overview
- 7. Image Processing and Computer Vision_Review：A survey of recent advances in visual feature detection—...
- 8. 深度學習的最新進展：綜述 Recent Advances in Deep Learning: An Overview
- 9. HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs--論文筆記
- 10. Call for Papers IEEE/ACM International Conference on Advances in Social Network Analysis and Mining