EfficientGCNv1 Paddle实现 1. 简介 EfficientGCN: Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition 一文提出了基于骨架行为识别的baseline,在论文中,将基于骨架识别的网络分为input branch和 main stream两部分。Input branch 用于提取骨架数据的多模态特征,提取的特征通过concat等操作完成...
AdaptiveGCN, an efficient and supervised graph sparsification framework. AdaptiveGCN adopts an edge predictor module to get edge selection strategies by learning the downstream task feedback signals for each GCN layer separately and adaptively in the training stage, then only inference with the s...
Finally, we introduce an efficient GCN inference accelerator, EGCN, specialized for minimizing off-chip memory access. EGCN achieves 41.9% off-chip DRAM access reduction, 1.49脳 speedup, and 1.95脳 energy efficiency improvement on average over the state-of-the-art accelerators.Yunki Han...
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本周阅读的论文题目是《Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks》,这是一篇在2019年发表在KDD上的一篇经典的文章,文章提出的模型名字叫Cluster-GCN。 …
读Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks,程序员大本营,技术文章内容聚合第一站。
训练两层GCN的VRGCN比Cluster-GCN快,但是却慢于增加一层网络但实现相似准确率的Cluster-GCN 在内存使用方面,VRGCN比Cluster-GCN使用更多的内存(对于三层的情况5倍多)。当训练4层GCN的时候VRGCN将被耗尽,然而Cluster-GCN当增加层数的时候并不需要增加太多的内存,并且Cluster-GCN对于这个数据集训练 4 层的GCN将实现...
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). - benedekrozemberczki/ClusterGCN
标题:GCNv2: Efficient Correspondence Prediction for Real-Time SLAM 作者:Jiexiong Tang, Ludvig Ericson, John Folkesson 来源:arXiv 编译:万应才 审核:李雨昊 欢迎个人转发朋友圈;其他机构或自媒体如需转载,后台留言申请授权 摘要 在本文中,我们提出了一个基于深度学习的网络——GCNv2,用于生成关键点和描述符。
the model. (Format:{attention}-resgcn-{structure}-{reduction}, attention:[pa, ca, fa, sa, pca, psa, None], structure:[b15, b19, b23, b29, n39, n51, n57, n75], reduction:[r1, r2, r4, r8, None], e.g., resgcn-b19, resgcn-n51-r4, pa-resgcn-b19, pa-resgcn-n51-...