SRGCAE
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[IEEE TGRS 2022] Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning
效果不佳
你好,我用你的代码在曙光数据集上进行试验,效果远不如你的,我对比生成的差分图,在局部关系上差分图效果特别差,而且DI融合得到的结果比后处理后的结果要好,请问你在实验中调整什么参数了吗?
Traceback (most recent call last): File "/home/dww/OD/others/SRGCAE-master/train_SRGCAE_Local.py", line 153, in train_model(args) File "/home/dww/OD/others/SRGCAE-master/train_SRGCAE_Local.py", line 76, in train_model feat_t1 = GCAE_model(node_t1, norm_adj_t1) File "/home/dww/anaconda3/envs/detection/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs)...
The following are the results I obtained after executing the file train_SRGCAE_Local.py.   I didn't adjust the parameters; why is the performance so poor?