This repository contains the architectures of different GANs/AEs that I have learned and implemented when I took GANs Specialization offered by DeepLearning.ai. I have experimented with various datasets as well and the results are attached inside the notebooks.
Apart from this, the repository also contains notes that I took during the specialization. I will keep the repository updated as I experiment with/implement new generative models' architectures other than the specialization.
| S.No | GAN | Paper |
|---|---|---|
| 1 | Basic GANs | Generative Adversarial Networks |
| 2 | Deep Convolutional GANs | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks |
| 3 | WGAN - with Gradient Penalty | Improved Training of Wasserstein GANs |
| 4 | Spectrally Normalized GANs | Spectral Normalization for Generative Adversarial Networks |
| 5 | Conditional GANs | Conditional Generative Adversarial Nets |
| 6 | Controllable GANs | Controllable Generative Adversarial Network |
| 7 | Info GAN | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets |
| 8 | Variational Autoencoder | Auto-Encoding Variational Bayes |
| 9 | Data Augmentation using GANs | Data Augmentation Generative Adversarial Networks |