InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://arxiv.org/abs/1606.03657

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

InfoGAN is an information-theoretic extension to the GAN that is able to learn disentangled representations in an unsupervised manner. InfoGANs are used when your dataset is very complex, when you’d like to train a cGAN and the dataset is not labelled, and when you’d like to see the most important features of your images.

University of Michigan - Ann Arbor

1. Deep Convolutional GANs (DCGANs)
2. Conditional GANs (cGANs)
3. StackGAN
4. InfoGANs
5. Wasserstein GANs(WGAN)

Different types of GANs

Introduction to Generative Adversarial Networks (GANs): Types, and Applications, and Implementation
https://heartbeat.fritz.ai/introduction-to-generative-adversarial-networks-gans-35ef44f21193

Introduction to Generative Adversarial Networks (GANs): Types, and Applications, and Implementation

DCGANs are an improvement of GANs. They are more stable and generate higher quality images. In DCGAN, batch normalization is done in both networks, i.e the generator network and the discriminator network. They can be used for style transfer. For example, you can use a dataset of handbags to generate shoes in the same style as the handbags.

Deep Convolutional GANs (DCGANs)

These GANs use extra label information and result in better quality images and are able to control how generated images will look. cGANs learn to produce better images by exploiting the information fed to the model.

Conditional GANs (cGANs)

The authors of this paper propose a solution to the problem of synthesizing high-quality images from text descriptions in computer vision. They propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. They decompose the hard problem into more manageable sub-problems through a sketch-refinement process.

StackGAN

InfoGANs

WGANs change the loss function to include a Wasserstein distance. They have loss functions that correlate to image quality.

Wasserstein GANs(WGAN)

The authors of this paper propose a method based on generative adversarial networks that learns to discover relations between different domains. Using the discovered relations, the network transfers style from one domain to another. In the process, it preserves key attributes such as orientation and face identity.


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