Comments (5)
+1. Tried to interpolate dlatent and the results didn't seem natural at all.
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So StyleGAN generator actually contains 2 components:
Generator:
qlatent = normally distributed noise which have shape=(512)
dlatent = mapping_network(qlatent) = shape=(18, 512)
where mapping_network - a fully connected network which transforms qlatent to dlatent
generator(mapping_network(qlatent)) = image
So during training we optimize dlatent instead of qlatent. Optimiziong of qlatent leads to bad results (I can elaborate on it). qlatent is used for features-wise transformation of convolution layers of generator https://distill.pub/2018/feature-wise-transformations/
2) dlatent + multiplier * logreg_coeff; Yes, but I use raw coefficients from logreg, so it doesn't matter are they positive or not.
3) Yes. It somehow works and we can gen relatively similar faces, but less details are saved. It still in progress.
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@vu0tran2 Yes I've seen but the "elaboration" was never given. In principle I don't see why it should be worse.
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I've done some experiments with optimizing dlatent vs qlatent. I've observed that when optimizing qlatent against a real image (I tried a few images of celebrities), the result does not converge to the desired target image. However, when optimizing qlatent against an image generated by sampling from qlatent space, the reconstruction converges quickly.
My intuition is that the space of qlatent is does not represent all human faces. Since qlatent has lower dimensionality than dlatent, it is intuitive to me (pigeonhole principle) that it is capable of representing fewer images.
from stylegan-encoder.
I've done some experiments with optimizing dlatent vs qlatent. I've observed that when optimizing qlatent against a real image (I tried a few images of celebrities), the result does not converge to the desired target image. However, when optimizing qlatent against an image generated by sampling from qlatent space, the reconstruction converges quickly.
My intuition is that the space of qlatent is does not represent all human faces. Since qlatent has lower dimensionality than dlatent, it is intuitive to me (pigeonhole principle) that it is capable of representing fewer images.
I tried to train the same encoding process and find the same problem.
Did you align the celebrities images? Since the generated images face landmarks are standard, which means eyes, mouth of all faces are exactly in the same place among all pictures.
I make lots of argumentation on generated images, the encoded result for real images become better but still far away from the same face.
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Related Issues (20)
- I just found a project that allows controlling a bunch of StyleGAN features through UI knobs: HOT 3
- About 'Google Drive quota exceeded' error. HOT 2
- colab cant run missing packages HOT 1
- Basic Usage Question
- tensorflow.python.framework.errors_impl.InvalidArgumentError
- Download couldn't happen from url, error in functions used in file named network.py and encode
- Download couldn't happen from url, error in functions used in file named network.py and encode_image.py
- How can I speed up the encode_image.py process? HOT 2
- why is an optimizer used in something that is not a neural network?
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- ModuleNotFoundError: No module named 'encoder'
- Generated images color was deformed HOT 1
- Syntax error in encode_images.py HOT 1
- latent directions
- Can't download LATENT_TRAINING_DATA HOT 7
- lines of code to crop and resave a collection of images to Gdrive
- Reduce encode_images.py time by using one model instance
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- Work with StyleGAN3?
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