基于此本文提出 LA-GNN(Local Augmentation-Graph Neural Networks),从特征表示和图拓扑尤其是局部子图结构的角度出发将数据增强视为特征生成过程,在给定中心节点的情况下学习其邻居特征的条件分布以生成特征来优化性能。 如果大家对大图数据上高效可扩展的 GNN 和基于图的隐私计算感兴趣,欢迎关注我的 Github,之后会不...
Local Augmentation for Graph Neural Networks 文章信息「来源」:Proceedings of the 39th International Conference on Machine Learning(ICML) 2022「标题」:Local Augmentation for Graph Neural Networks「作者」:Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Hua...
论文标题:Local Augmentation for Graph Neural Networks论文作者:Songtao Liu, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu论文来源:2021, arXiv论文地址:download 论文代码:download 1 Introduction现有的方法侧重于从全局的角度来增强图形数据,主要分为两种类型:...
Data augmentation has been widely used in image data and linguistic data but remains under-explored for Graph Neural Networks (GNNs). Existing methods focus on augmenting the graph data from a global perspective and largely fall into two genres: structural manipulation and adversarial training with ...
几篇论文实现代码:《Local Augmentation for Graph Neural Networks》(ICML 2022) GitHub: github.com/SongtaoLiu0823/LAGNN 《Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement...
We trained each affinity-based network with the same pipeline (for example, data augmentations and optimizer) and same hyper-parameters for each dataset. We also used the masks to restrict segmentation and evaluation to dense neuropil. These are the same masks used by FFN, thus comparing pure ...
Based on it, for each utterance, we build two graphs: local level graph and global level graph to account for the level-specific sentiment implications. Then, two graph contrastive learning strategies is adopted to explore the different potential presentations based on graph augmentations respectively...
[353], suggested a unique augmentation and synthetic image generation method for training deep neural net-based lesion diagnosis. Augmentation annotations are made less noisy by providing a reliability factor to crowd topics based on their results and requiring crowd area markings of interest. To ...
Data augmentation. We can add data in the less frequent categories by modifying existing data in a controlled way. In the example dataset, we could flip the images with illnesses, or add noise to copies of the images in such a way that the illness remains visible. Using appropriate metrics...
Emphasis is placed on action units within the facial regions [78,79], as they are crucial for emotion recognition. Moreover, data augmentation techniques such as cropping, resizing, flipping, rotation, and color adjustments are employed to expand the dataset and improve model generalization [8,17...