Comments (3)
Your are right, it is an error in the equation. I will update de arxiv. Thank you for pointing this out.
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Hi Albert, thanks for sharing your code.
In the paper, the fake image is generated by using the equation
.
While in the code, I find that the fake image is generated in a different way, which is
fake_imgs_masked = fake_img_mask * self._real_img + (1 - fake_img_mask) * fake_imgs
,
refers to https://github.com/albertpumarola/GANimation/blob/master/models/ganimation.py#L228 and https://github.com/albertpumarola/GANimation/blob/master/models/ganimation.py#L275, so it should be
Would you please kindly clarify this? Thanks in advance.
兄弟,你复现实验的时候有没有遇到attention趋于1的问题,在我实验的过程中论文里的两个约束loss起不到作用,attention还是会趋于1
from ganimation.
If it is tending to 1, suposing that the dataset is correct, you can try augmenting the lambda of this constraint. Be careful tunning it, if you increase it too much A will tend to 0. When I was tunning it I was aiming for a lambda that constrained A around 0.5.
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Related Issues (20)
- Is this how you would run your sample images ? HOT 2
- TypeError: iteration over a 0-d array
- Problem with Preparing annotation
- Pretrained Openface HOT 3
- About the paper figure
- A question about some loss functions in the paper
- Give “Attention” a name.
- Suggestion of using AU R-CNN instead of OpenFace for better AU detection accuracy.
- tensors must have same number of dimensions: got 2 and 3
- Attention Loss
- TypeError: Cannot handle this data type HOT 1
- when training “the loss_d_real is negative value ” is OK? and why
- Generator is different from that in paper HOT 1
- AUs as input parameter for trained model HOT 1
- IndexError: During training 0-dim tensor error HOT 2
- Can you share you train data in this paper? HOT 1
- Pretrained Model HOT 2
- Demo??
- Why the c_dim is 5 HOT 1
- How to get tar_aus in the batch ,what is the tar_aus?
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