Self-Attention Graph Pooling(SAGPool)是利用自注意力机制来区分应该删除的节点和应该保留的节点,基于图卷积计算注意力分数的自注意力机制,考虑了节点特征和图的拓扑结构。 SAGPool方法可以使用相对较少的参数以端到端方式学习层次表示,具有前几种方法的优点:分层池化,同时考虑节点特征和图的拓扑结构,合理的复杂度,以及...
attention包括self attention和target attention两种形式,这两种的计算步骤相同。在NLP领域,attention一般应用于encoder-decoder模型中,输入source和target的内容不同,比如文本翻译,输入source为中文,输出target为英文,当应用attention时,Q来自target,K和V来自source,则此时为target attention,当Q、K、V均来自target或source时,...
论文标题:Self-Attention Graph Pooling 论文作者:Junhyun Lee, Inyeop Lee, Jaewoo Kang 论文来源:2019, ICML 论文地址:download 论文代码:download 1 Preamble 对图使用下采样 downsampling (pooling)。 2 Introduction 图池化三种类型: Topology based pooling; ...
Self-Attention Graph Pooling is a method that moves the framework of deep learning to structured data, addressing the challenge of downsampling in graphs. The method introduces a self-attention mechanism to pool nodes in graphs, considering both node features and graph topology. This ap...
本文介绍的论文是《Self-Attention Graph Pooling》。 该篇文章提出了一种新的图池化方式,该池化方式是基于自注意力机制来计算得分,同时考虑了节点特征和图的拓扑结构,能够实现端到端方式学习图的层次表示。 🍁一、背景 近几年图神经网络已被广泛应用于各个领域,并且表现出了很好的性能,但是对于图进行采样操作仍是...
Self-attention: 给一个sequence,生成和sequence个数相同的输出。对key,value,query都有一组参数 W_k,W_v,W_q ,输入的向量分别相乘得到key,value和query. 特意提到了自己和自己的attention weight计算也很重要。 需要Position encoding来表明位置,每个位置有一个特定的向量,然后与输入向量相加。 语音辨识需要Truncate...
In general, many previous works overlook the design of the pooling strategy of the attention mechanism since they adopt the global average pooling for granted, which hinders the further improvement of the performance of the attention mechanism. However, we empirically find and verify a phenomenon ...
在图像上应用self-attention,定义查询像素和关键词像素q,k∈W×H,输入向量X大小为W*H*Din。 文章在这里为了保持一致性,用1D的符号来代表2D坐标,比如p=(i,j),用Xp代表Xij,用Ap代表Aij,替换后self-attention计算公式如下: POSITIONAL ENCODING FOR IMAGES ...
In this paper, we propose a graph pooling method based on self-attention. Self-attention using graph convolution allows our pooling method to consider both node features and graph topology. To ensure a fair comparison, the same training procedures and model architectures were used for the existing...
由此我们提出了一种 SAGPool模型,是一种 Self-Attention Graph Pooling method,我们的方法可以用一种End2End的方式学习结构层次信息,Self-attention结构可以区分哪些节点应该丢弃,哪些应该保留。因为Self-attention结构使用了Graph convolution来计算attention分数,Node features以及Graph topology都被考虑进去,简而言之,SAGPool...