FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻译
基于GNN的小样本学习 简介： 我们提出利用基于推理的图模型来研究小样本学习问题，输入图像有标签或者隐藏标签。通过使用消息传播算法，我们定义了一种图神经网络来概述最近提出的几种小样本学习的模型。除了提高数值性能之外，我们的框架还可以轻松扩展到少数学习的变体，例如半监督学习或主动学习，这证明了基于图的模型能够很好地处理“关系”任务。 介绍: 监督端到端学习是在CV,speech,机器翻译任务中是非常
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相关文章
- 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》翻译
- 5. 翻译--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笔记
- 8. Introduction to Graph Neural Network翻译-第四章Vanilla Graph Neural Networks
- 9. 笔记《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》-NeurIPS 2019
- 10. 论文翻译：Skeleton-Based Action Recognition with Directed Graph Neural Networks