FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻譯
基於GNN的小樣本學習 簡介： 我們提出利用基於推理的圖模型來研究小樣本學習問題，輸入圖像有標籤或者隱藏標籤。通過使用消息傳播算法，我們定義了一種圖神經網絡來概述最近提出的幾種小樣本學習的模型。除了提高數值性能之外，我們的框架還可以輕鬆擴展到少數學習的變體，例如半監督學習或主動學習，這證明了基於圖的模型能夠很好地處理「關係」任務。 介紹: 監督端到端學習是在CV,speech,機器翻譯任務中是非常
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相關文章
- 1. Few-shot Learning with Graph Neural Networks
- 2. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK
- 3. Few-Shot Learning with Graph Neural Networks
- 4. 《Neural Architecture Search with Reinforcement Learning》翻譯
- 5. 翻譯--SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 6. GRAPH2SEQ: GRAPH TO SEQUENCE LEARNING WITH ATTENTION-BASED NEURAL NETWORKS
- 7. Few-Shot Learning with Graph Neural Networks筆記
- 8. Introduction to Graph Neural Network翻譯-第四章Vanilla Graph Neural Networks
- 9. 筆記《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》-NeurIPS 2019
- 10. 論文翻譯：Skeleton-Based Action Recognition with Directed Graph Neural Networks