Comments (3)
@zsz00 Hi, 这个在论文的Ablation Study (sec.4.3.2)的Table 3中有相关实验,如果使用的是普通gcn,本质上是对邻域不断做加权平均,在网络加深的过程中会造成over-smoothing,在我们这个问题中,和mean-pooling的效果类似,都会造成性能的下降。有其它一些相关工作使用了更深的GCN,例如Combinational Optimization使用了20层GCN来提供NPHard的基础解, DeepGCNs在点云的语义分割上展示了可以使用更深的gcn
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把DeepGCNs的深层的网络 用在gcn-d中, 会有更好的效果吗?
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@zsz00 目前还没有做过相关的尝试,这两个方面的进展是互补的,欢迎尝试。
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Related Issues (20)
- output error HOT 1
- Dataset issue HOT 2
- Issue while training LGCN HOT 1
- 您好,想请教一下训练GCN-D时报错run out of input HOT 1
- 没有修改任何参数跑作者提供的pretrained model for ms1m Fscore 只有0.32
- Clustering 512D features using pretrained model HOT 3
- building symmetric adjacency matrix in utils HOT 1
- why using output of relu for rebuilding knn graph HOT 1
- is not a checkpoint filemodels/pretrained_gcn_e_ms1m.pth HOT 3
- about extracting features HOT 3
- About how to make a .meta file HOT 1
- Can we manually set the number of clusters during the test HOT 1
- 大佬,请教下train_cluster_seg_iop_vox.sh 的test阶段报错的问题 HOT 2
- 两个face recognition模型,推荐使用哪个,hfsoftmax还要用到python 2 ? HOT 1
- About pretrained_model to get the the extracted feature HOT 2
- GCN-D MMdistributedDataparallel train HOT 2
- 关于性能 HOT 1
- How to apply lgcn for clustering on custom embeddings of collected face images? HOT 1
- On custom dataset preparation
- On the numpy version which may cause exception HOT 3
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