Applyment of I2I(Style Transfer)

)

CartoonGAN

1.Style:normal->cartoon

2.Network Strucure

3.Loss Func

• Adversarial loss(!! without edge:fake)
  
• Content Loss：pretrianed VGG
  
• Total

4.Metric

• Qualitative :images

5.DataSets

• Photos:Flickr
• Cartton:key frames of cartoon films

6.Other contributes

• Initialization
  Pre-train the generator network G with only the semantic content loss Lcon(G;D).

Disentanged representaiton space in Content & Attribute

Diverse Image-to-Image Translation via
Disentangled Representations

1.Main

Better performance in I2I task:diverse realistic

2.Structure

3. Loss func

• Content Loss
  

• Cycle Loss
  

• Domain adversarial loss:
  L_domain=logD^d(u)+log(1-D^d(v))

• Self-reconstruction loss:L1 for
  
  *KL loss
  attribute distribution
  

• Cycle in Lantent space(for attribute distribution)
  
  

• all
  

4.Datasets

• Yosemite (summer and winter scenes),
• artworks (Monet and van Gogh)
• edge-to-shoes
• photo-to-portrait cropped from subsets of the WikiArt dataset 1
• Celeste deposit
• NIST [24] to MNIST-M [12]
• classication and pose estimation tasks with Synthetic Cropped LineMod to Cropped LineMod

5.Metric

Qualitative Evaluation

• transfered images
• Linear interpolation
  Quantitative Evaluation
• Realism:user study
• diversity:LPIPS metric
• Reconstruction:compare with paired data
• Domain Adaptation: Accuracy