Unsupervised Conditional Generation*Style Transfer
Conditional Generation*Style Transfer)
CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
1.Main Porpose
Unpaired Source&target Data for domain transfer
2.Structure
3.Loss Func
Photo generation from paintings :+Identity Loss
4.Metrics
- Qualitative：AMT studies
- Quantitative:FNC score:for labels->photo,FNC to detect labels in photo
- Quantitative:Standard metrics from Dataset, photo->labels:per pixel,per-class,class IOU
5.Datasets
- Cellection style transfer:Flicker Wikiart
- Object transfiguration ImageNet
- Season transfer Flickr
- Photo Enhancement:Flickr
StarGAN
1. Main Porpose
ConditionalGAN(Just Gene,No transfer)+CycleGAN(One side)
2.Structure
3. Loss Func
- Adversail loss
- Domain Classification loss
- Cycle loss
4. Metric
- Qualitative :give result pics
- Quantitative:AMT
- Quantitative:classification error of facial expression
5.DataSets
- CelebA
- RaFD
6.Other Contribution
Mask Vector for Multi Datasets trained all together
PairedCycleGAN
PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup
1. Main Porpose
Different style Make-up and tide-off for face,asymmetric Net
2 .Net Structure
3.Loss Func
- Adversarial loss
- Identity loss
- Style loss
- whether same makeup style
4 .Metric
- Qualitative:picture shown
- Quantitative:AMT on single/multi task
- Quantitative:classification of facial expression
5.Datasets
from Youtube makeup videos
