Comments (3)
The input feature is the value feature after fuser, with the dimension of 512
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ok,thank you
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The input feature is the value feature after fuser, with the dimension of 512
您好,当我将QAM加入到STCN当中时,且输入到Score模块的特征是fuse后的,但是训练得到的score值到后面都是0了.我是1GPU训练,batch size为16,学习率为1e-5
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Related Issues (17)
- I use QAM for training, the number of iterations is 75K, but the loss only drops to about 0.5, is this normal?And how much does the final loss value of the original code drop? HOT 12
- When the QAM is used in STCN,is the iterations the same as STCN? HOT 1
- 您好,请问论文中为什么不使用L2 distance而是dot product呢,是L2 distance效果比dot product差吗 HOT 2
- 您好,注意到您的代码变了,请问是之前的代码有问题吗? HOT 2
- 请问预训练阶段是不需要调整学习率的吗?代码中预训练时的steps比它的迭代次数还要大 HOT 6
- 您好,当我将QAM加入到STCN当中时,且输入到Score模块的特征是fuse后的,但是训练得到的score值到后面都是0了(Score模块里面全连接层的激活函数一个是relu,一个是sigmoid)我是1GPU训练,batch size为16,学习率为1e-5 HOT 7
- checkpoint file HOT 1
- train.py启动问题 HOT 5
- 关于预训练的问题 HOT 3
- Questions about pre-training HOT 19
- Do you train the whole network when you apply QAM in STCN? HOT 1
- May ask the result when you use the global average pooling layers in the code of QAM module? HOT 1
- youtube2018 HOT 2
- Adding QAM only in STM's inference stage HOT 1
- 为什么eval代码要用到label?
- The code has been reproduced, but the results are inconsistent with the provided model.
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