Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we ...
Deep Graph Similarity Learning: A Survey 来自 arXiv.org 喜欢 0 阅读量: 291 作者:G Ma,NK Ahmed,TL Willke,PS Yu 摘要: In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning ...
2.Inductive Learning归纳学习,使用GCN+GAE等方式,多个模型结合,达到更好的效果3.Similarity Measures相似度衡量的方式,还可以优化,比如L2-reconstruction loss,拉普拉斯特征映射,和Wasserstein距离,选择一个合适的相似度衡量方式很重要。参考的内容:AE和VAE:https😕/blog.csdn.net/sinat_36197913/article/details/...
Zhang Z, Cui P, Zhu W. Deep learning on graphs: A survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. 18年的一篇GNN综述,读完之后,感觉GCN那一部分对我帮助还不小,帮我理清了脉络,也可能是因为之前把《Graph Representation Learning》这本书看完了,所以阅读过程还比较顺利。后面的VG...
Self-supervised Learning(自监督学习):无监督学习的子集,用自动生成的标签对ConvNets进行明确训练。本文主要研究了基于卷积网络的视觉特征学习的自监督学习方法,该方法可以将特征迁移到多个不同的计算机视觉任务中。 由于在自我监督训练期间不需要人工标注来生成伪标签,因此可以使用非常大规模的数据集进行自我监督训练。在...
[197]作为先驱性的工作,首次提出了Similarity Group Proposal Network(SGPN)。该方法首先对每个点学习特征和语义map,接着引入相似度矩阵来表示各对点之间的相似度。为了学习到更多的判别式特征,使用了double-hinge loss来互相适应相似度矩阵和语义分割的结果。最后使用启发式的NMS方法将相似的点归并进一个实例中。由于...
Continual Graph Learning: A Survey 2023 Arxiv Towards Label-Efficient Incremental Learning: A Survey 2023 Arxiv Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation 2023 Arxiv How to Reuse and Compose Knowledge for a Lifetime of...
Step 1: The similarity between the query and each key is calculated to obtain the weight. Common similarity functions include the dot product, concatenating, and perceptron. The related descriptions are described as follows:(26)f(Q,K)=QTK,dotQTWaK,generalWa[Q;K],concatvattanh(WaQ+UaK),per...
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual label
Deep Learning for 3D Point Clouds: A Survey 论文阅读 Abstract:在点云深度学习中,主要包含的任务有:3D形状分类、3D目标检测和跟踪、3D点云分割。 Introduction:3D数据通常有许多种表现形式:深度图、点云、网格、体积网格(volumetric grids)。点云表示的好处是:保持了最原始的3D空间中的几何信息,并且没有任何的...