image-to-image-translation
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Hi, thanks for your great work.
I'm trying to apply stargan on my own custom anime dataset to learn facial expression transfer.
My dataset includes 2000 images per each emotions. (2k happy, 2k sad, etc)
I run the training process for about 100k iterations.
The problem is that I only see some small changes specially in mouth part. Do you have any suggestion? How can I make network more f
According to scipy, scipy.misc.toimage()
toimage is deprecated! toimage is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use Pillow’s Image.fromarray directly instead.
which is used on line 46 of utils/visualizer.py is now a deprecated function under the newest scipy version. As a result this co
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- Which are the 850 images mentioned in your paper?
Inside each experiment folder (SE0, SE1....SE28), there are 906 images. So to get the exact 850 images, you need to, first, reordering the image sequence according to the [SliceLocation] field of the Dicom image (sort in ascending order) and you will get images arranged from pelvis to head. Then just keeping slice 21 to 870 and discard the rest
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Which images should I use in the evaluation code?
I compared the generated images with Ground truth images and generated images with labels but got an error and the result of all the parameters was zero.
Generated images should be converted to label?
Solving this problem is very important to me.
thank you
![57882413-e101bd00-7838-11e9-8ffe-b0ef87f6e346](https://user-images.githubusercontent.c