Liu M Y, Tuzel O. Coupled generative adversarial networks[C]//Advances in neural information processing systems. 2016: 469-477.
1. Overview
In this paper, it proposed coupled GAN
- based on existence of shared high-level representation in the domains
- learn a joint distribution of multi-domain images
- unsupervised
- used in image transformation, domain adaption…
1.1. Model
Generator
- share the same high-level concept
Discriminator - sharing constraint can reduce parameters
1.2. Loss Function
1.3. Related Work
- VAE
- Attention Model
- Moment Matching
- Diffusion Process
- Cross-domian Image Generation
- GAN
- Laplacian Pyramid
- Conditional GAN
2. Experiments
2.1. Metric
- ratios of agreed pixels
2.2. Digit
- [digit-edge], [digit-negative]
- without weight-sharing constraint, GAN generate unrelated image
- correlated to the weight sharing of G
- uncorrelated to D
2.3. Face
2.4. Color and Depth Image
3. Application
3.1. Unsupervised Domain Adaption (UDA)
- [MNIST(labeled)-UDA(unlabeled)]
- attached a softmax layer c to last hidden layer of D, train on MNIST, predict on UDA
3.2. Cross-Domain Image Transformation
Given x1 in domain 1, find corresponding image x2 in domain 2. As for CoGAN
- get the most suitable z* for x1
- use z_* generate x2