You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy 
Chapter Title:  Fully-connected GAN (FCGAN) 
1. Abstract of Cited Paper (BIB001):  We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

Literature Review:
The original GAN paper BIB002 uses fully-connected neural networks for both generator and discriminator. This architecture-variant is applied for some simple image datasets i.e., MNIST BIB001 , CIFAR-10  and Toronto Face Dataset. It does not demonstrate good generalization performance for more complex image types.