Pixel-Adaptive Convolutional Neural Networks CODE:https://suhangpro.github.io/pac/ 摘要 卷积是cnn的基本组成部分。它们的权重在空间上是共享的,这是它们广泛使用的一个主要原因,但这也是一个主要的限制,因为它使得卷积不可知论的争论。我们提出了一种像素自适应卷积(PAC)操作,这是对标准卷积的一种简单而有效的...
Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in ...
TOPOLOGY ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS(TAGConv 论文笔记) 论文链接:arxiv.org/abs/1710.1037 摘要 谱图卷积神经网络(Spectral graph convolutional neural networks, CNNs)需要对卷积进行近似,以降低计算复杂度,导致性能损失。本文提出了拓扑自适应图卷积网络(TAGCN),这是一种定义在顶点域上的图卷积网络。我们...
为了利用跨域图来学习用于节点分类的分类器,文章提出了一种无监督的域自适应图卷积网络(Unsupervised Domain Adaptive Graph Convolutional Networks, UDA-GCN),以缩小分布差距并产生跨域共享的低维特征表示。如图2所示,UDA-GCN框架主要由以下三个组件构成: 节点表示学习。为更好地学习每个节点的表示,使用了一种对偶图卷...
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks 阅读笔记,程序员大本营,技术文章内容聚合第一站。
论文标题:Unsupervised Domain Adaptive Graph Convolutional Networks论文作者:论文来源:2020 aRxiv论文地址:download 论文代码:download视屏讲解:click 1-摘要图卷积网络(GCNs)在许多与图相关的分析任务中都取得了令人印象深刻的成功。然而,大多数 GCN 只在一个域(图)中工作,无法将知识从其他域(图)转移,因为图表示...
Subsequently, it integrates graph convolutional neural networks (GCNs) with long short-term memory (LSTM) networks to capture the spatiotemporal characteristics of traffic data. Additionally, a GCN is designed to capture the spatial topological relationships of the road network. Finally, a novel ...
论文阅读:AM-GCN Adaptive Multi-channel Graph Convolutional Networks ABSTRACT 图卷积网络(GCNs)在处理各种图和网络数据的分析任务中得到了广泛的应用,但最近的一些研究提出了GCNs能否在信息丰富的复杂图中最优地集成节点特征和拓扑结构的问题。在本文中,我们首先进行了实验研究。令人惊讶的是,我们的实验结果清楚地表明...
Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks. However, there are difficulties in transfer learning for graph representation learning and graph network models. Most GNNs work only in a single domain and cannot trans...
In summary, we propose a novel spatio-temporal adaptive graph convolutional networks model (STAGCN) for traffic flow forecasting. The model automatically captures spatio-temporal correlations in traffic sequences and enables adaptive modelling of the spatial topology graph of road networks. The main in...