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it seems that the D tries to distinguish between a fake encoded B and a real image, but from a different image file in the code above.
so why not use self.real_B_random = self.real_B[0:half_size] which is the same image instead.
if the 2 images are very random: the 1st encoded image is from a "sneaker" and the 2nd real random image is a "high heel", then such a way is benefit to improve D?
The text was updated successfully, but these errors were encountered:
This is a minor implementation detail that should not significantly affect the results. We chose to use different images for training cVAE-GAN and training cLR-GAN. That is also the reason for batchSize=2. I also recently added a new model in the dev branch that uses the same real image for both cVAE-GAN and cLR-GAN.
If two images are random, D tries to learn the difference between real vs. fake shoes. In both cases, D only takes the images from domain B as its input. D is not conditional on the input image from domain A.
For the line below, as the batch size =2. So the two images in self.real_B are different.
self.real_B_random = self.real_B[half_size:]
BicycleGAN/models/bicycle_gan_model.py
Line 100 in 9cd9081
BicycleGAN/models/bicycle_gan_model.py
Line 178 in 9cd9081
it seems that the D tries to distinguish between a fake encoded B and a real image, but from a different image file in the code above.
so why not use self.real_B_random = self.real_B[0:half_size] which is the same image instead.
if the 2 images are very random: the 1st encoded image is from a "sneaker" and the 2nd real random image is a "high heel", then such a way is benefit to improve D?
The text was updated successfully, but these errors were encountered: