GANs are one of the hottest topics in deep learning nowadays! The GAN framework has many strengths compared to other frameworks that we will enumerate in this section. Naturally, GANs also have weaknesses and challenges that we will describe.
One of the advantages of GANs is the use of the discriminator as an embedding space that does not require any label. This has been described in the paper Feature learning by inpainting by Deepak Pathak et al. and Unsupervised representation learning with deep convolutional generative adversarial networks by Alec Radford et al. In these papers, the authors have used GANs to learn features in an unsupervised fashion. Another amazing strength of the GAN framework is that it circumvents the potentially difficult challenge of designing an objective function for the task at hand.
There are also many weaknesses...