論文《Unsupervised Continual Learning And Self-Taught Associative Memory Hierarchies》解讀
本論文提出了一個無監督的持續學習組件STAM(Self-Taught Associative Memory) 如下圖： 上圖中， C i C_i Ci爲所有聚類簇的集合。當一個圖片 X i X_i Xi輸入到這個結構中時，會被打碎成若干個重疊的可接受域(Receptive Fields,RFs)。需要注意的是，每一個STAM結構處理一個可接受域。這些可接受域經過函數 c ( x i , m )
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相關文章
- 1. 【論文筆記】Continual Relation Learning via Episodic Memory Activation and Reconsolidation
- 2. 論文閱讀：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 3. 論文理解—— Disentangle-based Continual Graph Representation Learning
- 4. 持續學習——Continual Unsupervised Representation Learning——NeurIPS2019
- 5. Continual Learning 經典方法：Memory Aware Synapses (MAS)
- 6. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之論文詳解
- 7. Supervised learning and Unsupervised learning
- 8. Unsupervised Learning of Edges 論文閱讀
- 9. 論文閱讀： GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose(CVPR2018)
- 10. 論文閱讀——Mockingjay: unsupervised speech representation learning