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Frameworks

  1. GAN, DCGAN
  2. cGAN - Label-conditioning generation
  3. AcGAN - Supervised generation (auxiliary classifier with labels)
  4. SGAN - D outputs [CLASS-1, CLASS-2, . . . CLASS-N, FAKE]
  5. InfoGAN - Unsupervised lantern space disentangling
  6. ALI, BiGAN - match p(G(z),z) and q(x,E(x))
  7. GMAN - Multiple discriminator models <!-- TODO -->
  8. AdaGAN - Multiple generative models <!-- TODO -->
  9. MGGAN - Multiple generative models <!-- TODO -->

High Resolution

  1. LAPGAN - Coarse-to-fine generation
  2. StackGAN - Two-step generation
  3. PGGAN

Function ('~' means 'match')

g(Z) ~ X

  1. GAN, DCGAN

g(Z|Y) ~ X|Y, where Y is label or lantern variable

  1. cGAN
  2. AcGAN
  3. InfoGAN
  4. CFGAN

g(Z),Z ~ X,E(X)

  1. ALI, BiGAN

Objective Function

f-Divergence

  1. GAN, DCGAN - JS divergence
  2. LSGAN (Least Squares GAN) - Pearson χ2 divergence
  3. f-GAN - Variational divergence minimization
  4. f-GANs

Integral Probability Metrics (IPM)

  1. WGAN - Wasserstein distance
    • WGAN-GP - Gradient penalty, less capacity compromise
  2. McGAN - Mean and covariance matching
  3. Geometric GAN
  4. Fisher GAN - Chi-squared distance
Maximum Mean Discrepancy (MMD)
  1. GMMN
  2. MMD nets

Others

  1. CatGAN - Entropy of P(Y|X)
  2. EBGAN - D(x) = ||Dec(Enc(x)) − x||
  3. LS-GAN (Loss-Sensitive GAN)
  4. BEGAN - Wasserstein distance between the distribution of real/fake auto-encoder loss
  5. AGE - $max_emin_g\Delta(e(g(Z))||Y)-\Delta(e(X)||Y)$ and encoder-generator reciprocity (bidirectional mapping)
  6. Softmax GAN
  7. Cramér GAN - Cramér Distance
  8. LDGAN

Regularized GAN

  1. DRGAN - Vanilla GAN with gradient penalty
  2. Cramér GAN - Cramér Distance
  3. Regularized GAN
  4. CT-GAN
  5. Varying k-Lipschitz Constraint for Generative Adversarial Networks

Representation Learning

Lantern Space Disentangling

  1. InfoGAN - Unsupervised lantern space disentangling
  2. AcGAN - Supervised lantern space disentangling

Specifying Lantern Space Distribution

  1. AAE
  2. ALI, BiGAN - Match p(G(z),z) and q(x,E(x)), simultaneously learn an encoder and decoder
  3. AGE

Semi-supervised Learning

  1. CatGAN
  2. SGAN - D outputs [CLASS-1, CLASS-2, . . . CLASS-N, FAKE]
  3. Improved GAN
  4. Triple-GAN
  5. CT-GAN

Evaluations of GANs <!-- TODO -->

  1. Inception Score - Improved GAN
  2. FCN Score - pix2pix
  3. AMT Perceptual Studies - pix2pix
  4. Semantic Segmentation Metrics - CycleGAN
  5. FID, Precision, Recall and F1 Score - Are GANs Created Equal? A Large-Scale Study

Applications

Data Augmentation

  1. Adversarial Generation of Training Examples for Vehicle License Plate Recognition

Domain Adaptation

  1. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
  2. DTN
  3. UNIT
  4. CoGAN

Image Denoising

  1. Deep Semantic Face Deblurring

Image-to-Image Translation

Unpaired
  1. CoGAN
  2. DTN
  3. CycleGAN, DiscoGAN, DualGAN
  4. UNIT
  5. Face Transfer with Generative Adversarial Network
  6. XGAN - Semantic consistency
Paired
  1. pix2pix/PatchGAN
  2. Scribbler
  3. PAN - Perceptual adversarial loss
  4. Cross-View Image Synthesis using Conditional GANs

Inpainting

  1. Context Encoder
  2. PAN

Super-Resolution

  1. SRGAN

Text-to-Image Synthesis

  1. GAN-INT-CLS <!-- TODO -->
  2. GAWWN <!-- TODO -->
  3. StackGAN

Face Editing

  1. VAE/GAN - Visual attribute vectors
  2. IcGAN
  3. DIAT
  4. Learning Residual Images for Face Attribute Manipulation
  5. DistanceGAN
  6. CFGAN
  7. Age-cGAN
  8. UNIT
  9. SL-GAN
  10. IAN
  11. Neural Face Editing with Intrinsic Image Disentangling
  12. GeneGAN - Object transfiguration
  13. Fader Networks
  14. Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
  15. ExprGAN
  16. StarGAN
  17. GLCA-GAN

Face Frontalization/Profiling

  1. DR-GAN
  2. TP-GAN

Others

  1. iGAN - Image manipulation
  2. TVSN - 3D view synthesis <!-- TODO -->
  3. ID-CGAN - Image de-raining
  4. Perceptual GAN - Small object detection

Survey

  1. Generative Adversarial Networks: An Overview
  2. How Generative Adversarial Nets and its variants Work
  3. Comparative Study on Generative Adversarial Networks
  4. An Introduction to Image Synthesis with Generative Adversarial Nets

Unclassified

  1. DeePSiM
  2. Unrooled GAN
  3. SGAN
  4. AM-GAN
  5. DeLiGAN - Mixture Gaussian prior distribution
  6. TTUR

Datasets

  1. MNIST
  2. CIFAR10/CIFAR100
  3. SVHN
  4. CelebA