Single Image Super-Resolution via Non-Local Normalized Graph Laplacian Regularization: A Self-similarity TributeSuper-resolutionRegularizationImage restorationImage zoomingGraph laplacianSelf-similarityFractal-
Graph Laplacian Regularized Graph ConvolutionalNetworks for Semi-supervised LearningBo Jiang, Doudou LinSchool of Computer Science and TechnologyAnhui UniversityHefei, Chinajiangbo@ahu.edu.cnAbstractRecently, graph convolutional network (GCN) has been widely used for semi-supervised classif i cation and de...
本文通过使用 Graph Laplacian Regularization 来建模图结构和节点特征。其假设相连的两个节点更有可能属于同一类,从而迫使相邻节点具有相似的节点嵌入。这种简单的启发式假设无法涵盖图中节点的复杂关系,特别是位于决策边界附近的节点,其实际上是预测不可靠的节点。因此引入边可靠度作为正则化的一种方法。
This amounts to solving a problem with a scaled spectrum of the Laplacian. The regularization constant \(\omega\) in (17) balances the interplay between the data misfit term and the structure-preserving regularization term. If its value is too small, the multi-fidelity model is likely to ...
Graph Laplacian Regularization: In machine learning, the Laplacian matrix is commonly used for regularization in graph-based semi-supervised learning, where the goal is to predict labels for nodes in a graph using a combination of labeled and unlabeled data.Example...
Deep Graph Laplacian Regularization for Robust Denoising of Real Images Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung CVPR 2019 Learning Context Graph for Person Search Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang ...
通过混合图的拉普拉斯正则化逐行图像复原 翻译结果2复制译文编辑译文朗读译文返回顶部 至混合的图表 Laplacian Regularization 的进步的图像复原 翻译结果3复制译文编辑译文朗读译文返回顶部 通过混合图拉普拉斯经常化的渐进式图像恢复 翻译结果4复制译文编辑译文朗读译文返回顶部 ...
2. 代码实现(Pytorch):https://github.com/tkipf/pygcn 【Introduction】: 本文尝试用 GCN 进行半监督的分类,通过引入一个 graph Laplacian regularization term 到损失函数中: 其中,L0 代表损失函数,即:graph 的标注部分,f(*) 可以是类似神经网络的可微分函数,X 是节点特征向量组成的矩阵, 代表 无向图 g ...
In this paper, a semi-supervised framework based on graph Laplacian and mixed convex and non-convex l2,p-norm (0 < p ≤ 1) regularization is proposed for regression problems. In the proposed framework, a semi-supervised graph Laplacain based scatter matrix constructed for regression ...
在这类问题中,由于大部分节点都没有已标注的标签,因此往往需要使用某种形式的图正则项对标签信息进行平滑(例如在损失函数中引入图拉普拉斯正则(graph Laplacian regularization)): 其中, \mathcal{L}_{0} 表示有监督的损失, f(·) 可以是一个类似于神经网络的可微函数。 \lambda 表示一个权值因子, X 则是相应...