论文《Unsupervised Continual Learning And Self-Taught Associative Memory Hierarchies》解读
本论文提出了一个无监督的持续学习组件STAM(Self-Taught Associative Memory) 如下图： 上图中， C i C_i Ci为所有聚类簇的集合。当一个图片 X i X_i Xi输入到这个结构中时，会被打碎成若干个重叠的可接受域(Receptive Fields,RFs)。需要注意的是，每一个STAM结构处理一个可接受域。这些可接受域经过函数 c ( x i , m )
相关文章
- 1. 【论文笔记】Continual Relation Learning via Episodic Memory Activation and Reconsolidation
- 2. 论文阅读：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 3. 论文理解—— Disentangle-based Continual Graph Representation Learning
- 4. 持续学习——Continual Unsupervised Representation Learning——NeurIPS2019
- 5. Continual Learning 经典方法：Memory Aware Synapses (MAS)
- 6. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之论文详解
- 7. Supervised learning and Unsupervised learning
- 8. Unsupervised Learning of Edges 论文阅读
- 9. 论文阅读： GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose(CVPR2018)
- 10. 论文阅读——Mockingjay: unsupervised speech representation learning
- 更多相关文章...
- • C# 文本文件的读写 - C#教程
- • *.hbm.xml映射文件详解 - Hibernate教程
- • JDK13 GA发布：5大特性解读
- • Scala 中文乱码解决
相关标签/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。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 【论文笔记】Continual Relation Learning via Episodic Memory Activation and Reconsolidation
- 2. 论文阅读：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 3. 论文理解—— Disentangle-based Continual Graph Representation Learning
- 4. 持续学习——Continual Unsupervised Representation Learning——NeurIPS2019
- 5. Continual Learning 经典方法：Memory Aware Synapses (MAS)
- 6. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之论文详解
- 7. Supervised learning and Unsupervised learning
- 8. Unsupervised Learning of Edges 论文阅读
- 9. 论文阅读： GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose(CVPR2018)
- 10. 论文阅读——Mockingjay: unsupervised speech representation learning