Comments (1)
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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Related Issues (20)
- MODNet离线demo——WebCam调用摄像头延迟 HOT 1
- Will you please release the DEMO trained model? HOT 1
- Anyone who would kindly share their training code? HOT 5
- Training Code HOT 9
- 如何设置只对人像进行识别抠图 HOT 2
- Initial performance very poor after training on P3M dataset.
- 你好,我是清华的一名学生,论文提到分割部分的backbone是mobileV2吗,这部分是否可以换成其他网络,有一些细节比较困惑,真心请教,感激不尽 HOT 1
- Whether it is possible to replace the backbone of the semantic segmentation part and change it to swintrainsformer
- inaccurate
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- How to get the label(groundtruth) of other image?
- 请问mobilenetv2_human_seg.ckpt和其他几个模型的区别在哪儿?
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- edit the LRBranch and HRBranch after changed backbone HOT 3
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- 求教
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