Extensive review and comparison of CNN and GAN

CNNs and GANs, together and separately, achieve groundbreaking developments in artificial intelligence while they play prominent roles as deep learning structures. This document is a rather extensive overview and side-by-side analysis of CNNs and GANs and their back-end architectures and workings, as well as their advantages and disadvantages and uses in practice. Convolutional Neural Networks (CNNs), known for their outstanding feature extraction capabilities, have greatly boosted up the scope of image classification, object detection, and medical diagnostics; Generative Adversarial Networks (GANs) have brought a new generalized approach to generative modelling, generating extremely realistic images, videos, and data. This analysis highlights significant differences in the training of CNNs and GANs, intricacy of the latter two’s architectures, and metrics used to measure performance, as well as recurrent challenges such as overfitting in CNNs and instability in GANs. Furthermore, the paper explores how these models can be coupled to form hybrid systems and perform better in such applications as data augmentation and image translation. This paper will attempt to provide an in-depth review of these models to give researchers and practitioners a clear spectacle to use these models across various applications and determine areas that future research can be directed.