Comments (3)
Yes, the entire work/paper is about unaligned data. Alignment is not required.
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The shared latent space assumption is an assumption saying that if there exists a pair of corresponding images in the two domains then the two images should have the same latent representation. During training, you don’t have aligned data (paired of corresponding images). The whole point of the paper is to use this constraint to encourage a solution that would be likely desired.
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@mingyuliutw Could you please tell me which part of the model is preventing it from giving the same shared latent representation when the pair of input images is not a pair of corresponding image e.g. image 1 is the number 6 from SVHN and image 2 is the number 7 from MNIST?
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Related Issues (20)
- Unable to reproduce your gta2city results using parallel GPUs HOT 1
- Importance of data augmentation HOT 1
- [paper] extra single bracket HOT 1
- Image links are 404 in TUTORIAL.md HOT 1
- Train with lower precision HOT 1
- Hello, I am trying to train my own dataset, but how are the image naming rules under the trainA and trainB folders named? HOT 1
- Winter<->Summer Pretrained model or dataset. HOT 1
- Problem of train
- Conversion from visual images to infrared images HOT 1
- Testing on a single image HOT 2
- Difference between results with/without shared weights HOT 2
- THEORETICAL QUESTION ABOUT THE MODEL HOT 1
- Pytorch does not support torch.utils.serialization anymore HOT 1
- question about testing。 HOT 1
- Error to import torch.utils.serialization HOT 2
- NotFoundError: NewRandomAccessFile failed to Create/Open: images/labelmap.pbtxt : The system cannot find the file specified; No such file or directory HOT 1
- I can not find day2night weights.
- question about training HOT 1
- Weight sharing part
- vgg16.t7 pretrained model?
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