Comments (2)
MUNIT and DRIT are concurrent works sharing a similar high-level idea. The key differences lie in the way they combine content and attribute features. MUNIT adopts AdaIn to transform content features based on attribute features, while DRIT provides two options: 1. For color-variation translation, simple concatenation works well. 2. For shape-variation translation, we adopt element-wise feature transformation.
from drit.
MUNIT and DRIT are concurrent works sharing a similar high-level idea. The key differences lie in the way they combine content and attribute features. MUNIT adopts AdaIn to transform content features based on attribute features, while DRIT provides two options: 1. For color-variation translation, simple concatenation works well. 2. For shape-variation translation, we adopt element-wise feature transformation.
Thanks, I understood it.
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Related Issues (20)
- DRIT++ Or DRIT
- Change Input Size error HOT 1
- code about self-reconstuction loss ? HOT 1
- RuntimeError when training HOT 12
- How can I train with multi-gpus? HOT 2
- DRIT++ performs surprisingly poor on edges2shoes
- content discriminator training vs other parts training HOT 2
- Comparing Different models
- Using batch size > 2 (half size > 1)
- what's the version of tensorflow and tensorboardX?
- Confusion about the formula of content adversarial loss HOT 1
- what's the meaning of random_type='gauss'? HOT 1
- How can i run this model in example guided translation?
- Download datasets link error HOT 1
- Can you provide the curve during training?
- Can you provide the curve during training?
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation HOT 3
- the link of project page is invalid...
- The pre-trained model on Yosemite you provided no longer exits.
- The link of download dataset not exist
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