Comments (2)
junyanz
commented on July 19, 2024
Thanks for your interest in our work. The main advantage of BicycleGAN is that it can produce multiple outputs given the same input while pix2pix and Cyclegan can only produce 1 result. For your other two questions:
- The performance is similar.
- It uses bidirectional loss between the latent space and the output image space. The training is slightly slower than pix2pix as it also learns to discover multiple modes.
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lxj616
commented on July 19, 2024
Thank you for your reply, all questions are clear, issue closed : )
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Related Issues (20)
- Why does not large batch size like 128, 256 work well?
- Hi, please help me.
- Question about PatchGAN perception field HOT 4
- Why my LPIPS distance is larger than what your paper say? HOT 3
- Question about conditional_D implementation HOT 3
- Not clear in the difference between the two latent spaces predicted HOT 5
- Test on single images HOT 1
- Compute graph wrong and one question HOT 4
- test_before_push is a great rapid test file, but seems outdated? HOT 3
- Metric reporting HOT 1
- Question about Encoder in cLR-GAN HOT 1
- Question about generating fake_B_random
- Question: Do you need two separate discriminators? HOT 2
- Incorrect discriminator update for opt.use_same_D HOT 1
- Regarding Training your Own Images HOT 4
- How to train on large images?
- Is there <pix2pix+noise> model code that can be directly run？ HOT 1
- diversity question
- TypeError: __init__() got an unexpected keyword argument 'nl_layer'
- Regarding Latent Space Interpolation
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