Comments (1)
ZHKKKe
commented on June 2, 2024
你好，感谢你的关注。
对于你的问题：
Q1: 那么在做反向传播的时候，梯度是从两个输出同时传进去的吗
梯度会从两个项传回去。以前面的l2损失为例，在实现时候需要将这个l2损失拆解为两项进行计算。每一项中需要detach其中一个网络输出作为标签计算梯度，否则，PyTorch会返回错误。
Q2: 将M复制给M‘后，M’在后面权重是一直不变的吗
时的， M' 在SOC中只是为保持细节提供一定的约束，它的权重始终固定。
Q3: 如果前面训练好的M网络对于新域的视频数据预测的很差，那么结果会不会朝不好的方向发展呢？
我们发现，在Matting中，网络对新域的视频数据预测的很差主要表现在人像语义估计上。细节估计可以被看作一个以人像语义为先验的low-level的视觉任务，通常，将网络应用在新数据域上依旧可以较好的处理细节。
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