1. Introduction

2. Generative Adversarial Network (GAN)  
2.1. Adversarial Learning: The Adversarial Game  
2.2. Optimization and Loss Function  
2.3. Network Structure of GAN  
2.4. Optimal Solution of GAN  
2.5. Convergence and Equilibrium Analysis of GAN  
2.6. Conditional GAN  
2.7. Deep Convolutional GAN (DCGAN)  

3. Mode Collapse Problem in GAN  
3.1. Mode Collapse Problem  
3.2. Minibatch GAN  
3.3. Unrolled GAN  
3.4. Bourgain GAN (BourGAN)  
3.5. Mixture GAN (MGAN)  
3.6. Dual Discriminator GAN (D2GAN)  
3.7. Wasserstein GAN (WGAN)  

4. Maximum Likelihood Estimation in GAN  
4.1. Comparison of MLE and GAN  
4.2. f-GAN  
4.3. Adversarial Variational Bayes (AVB)  
4.4. Bayesian GAN (BGAN)  

5. Other Variants of GAN  
5.1. Feature Matching in GAN  
5.2. InfoGAN  
5.3. Generative Recurrent Adversarial Network (GRAN)  
5.4. Least Squares GAN (LSGAN)  
5.5. Energy-based GAN (EBGAN)  
5.6. Semi-supervised GAN  
5.7. MMD GAN  
5.8. Additive GANs  
5.9. Triple GAN  
5.10. Latent Adversarial Generator (LAG)  
5.11. Ensembles of GAN Models  
5.12. Coupled GAN (CoGAN)  
5.13. Inverse GAN Models  
5.14. Self-Attention GAN (SAGAN)  
5.15. Few-shot GAN Models  
5.16. Training Triplet Network with GAN  

6. Sampling and Interpolation in GAN  
6.1. Interpolation in the Latent Space  
6.2. Manifold Interpolated Neighbor Embedding  
6.3. Analogy and J-Diagram  
6.4. Attribute Vector  
6.5. Evaluation of Generated Images  

7. Applications of GAN  
7.1. Image-to-Image Translation by GAN  
7.2. Text-to-Image Generation  
7.3. Mixing Image Characteristics  
7.4. Other Applications  

8. Autoencoders Based on Adversarial Learning  
8.1. Adversarial Autoencoder (AAE)  
8.2. PixelGAN Autoencoder  
8.3. Implicit Autoencoder (IAE)  

9. Conclusion