There are currently thousands of different GANs available and this number is increasing at a phenomenal rate. In this section, we will explore six popular GAN architectures, which we will cover in more detail in the subsequent chapters of this book.

Alec Radford, Luke Metz, and Soumith Chintala proposed deep convolutional GANs (DCGANs) in a paper titled Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, which is available at the following link: https://arxiv.org/pdf/1511.06434.pdf. Vanilla GANs don't usually have convolutional neural networks (CNNs) in their networks. This was proposed for the first time with the introduction of DCGANs. We will learn how to generate anime character faces using DCGANs in Chapter 3, Face Aging Using Conditional GANs.

StackGANs were proposed by Han Zhang, Tao Xu, Hongsheng Li, and others in their paper titled StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks, which is available at the following link: https://arxiv.org/pdf/1612.03242.pdf. They used StackGANs to explore text-to-image synthesis with impressive results. A StackGAN is a pair of networks that generate realistic looking images when provided with a text description. We will learn how to generate realistic looking images from text descriptions using a StackGAN in Chapter 6, StackGAN – Text to Photo-Realistic Image Synthesis.

CycleGANs were proposed by Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros in a paper titled Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, which is available at the following link: https://arxiv.org/pdf/1703.10593. CycleGANs have some really interesting potential uses, such as converting photos to paintings and vice versa, converting a picture taken in summer to a photo taken in winter and vice versa, or converting pictures of horses to pictures of zebras and vice versa. We will learn how to turn paintings into photos using a CycleGAN in Chapter 7, CycleGAN - Turn Paintings into Photos.

3D-GANs were proposed by Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum in their paper titled Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling, which is available at the following link: https://arxiv.org/pdf/1610.07584. Generating 3D models of objects has many use cases in manufacturing and the 3D modeling industry. A 3D-GAN network is able to generate new 3D models of different objects, once trained on 3D models of objects. We will learn how to generate 3D models of objects using a 3D-GAN in Chapter 2, 3D-GAN - Generating Shapes Using GAN.

Face aging with Conditional GANs was proposed by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay in their paper titled Face Aging with Conditional Generative Adversarial Networks, which is available at the following link: https://arxiv.org/pdf/1702.01983.pdf. Face aging has many industry use cases, including cross-age face recognition, finding lost children, and in entertainment. We will learn how to train a conditional GAN to generate a face given a target age in Chapter 3, Face Aging Using Conditional GAN.

The pix2pix network was introduced by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros in their paper titled Image-to-Image Translation with Conditional Adversarial Networks, which is available at the following link: https://arxiv.org/abs/1611.07004. The pix2pix network has similar use cases to the CycleGAN network. It can convert building labels to pictures of buildings (we will see a similar example in the pix2pix chapter), black and white images to color images, images taken in the day to night images, sketches to photos, and aerial images to map-like images.