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Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
時間 2021-01-02
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未完。。。 本文提出了一種用於多標籤零樣本學習(ML-ZSL)的深度學習架構,它能夠爲每個輸入實例預測未知的類標籤。受人類利用對象之間語義知識方式的啓發,我們提出了一個框架,包含用於描述多個標籤之間關係的知識圖譜。我們的模型從語義標籤空間學習信息傳播機制,該機制可用於建模已知和未知類標籤間的相互依存關係。 通過對視覺推理的結構化知識圖譜的研究,我們證明了我們的模型可用於解決多標籤分類和ML-Z
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相關文章
1.
【譯】Deep Learning with Knowledge Graphs
2.
《Multi-Label Zero-Shot Learning With Structured Knowledge Graphs》論文筆記
3.
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs 論文筆記
4.
【論文筆記】Representation Learning of Knowledge Graphs with Entity Descriptions
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讀書筆記3:Representation Learning on Graphs with Jumping Knowledge Networks
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論文筆記:Representation Learning of Knowledge Graphs with Entity Descriptions
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【PaperReading】Text Generation from Knowledge Graphs with Graph Transformers
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9.
Explainable Reasoning over Knowledge Graphs for Recommendation
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