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Comprehensive Analysis and Comparison of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs)
Subject area: Science,Engineering and Technology · Area of research: Convolutional Neural Networks (CNN)
Abstract
CNNs and GANs, whether working solo or combined, have sparked major shifts in AI, each playing distinct roles in the deep learning landscape. This write-up dives into a fairly deep dive and comparison of both models, digging into their internal designs, pros and cons, and practical uses. CNNs, famous for their knack for pulling out visual features, have pushed forward image tasks like classification and detection, along with medical imaging. Meanwhile, GANs shook up the generative scene by producing incredibly lifelike visuals and data. Along the way, this review uncovers key differences in how CNNs and GANs are trained, how complex GANs can get, and the ways their performance gets measured?while also pointing out common hurdles like CNN overfitting or GAN instability. On top of that, it looks into how mixing the two can lead to hybrid systems, useful in areas like synthetic data generation or style transfer. The aim here is to give researchers and engineers a helpful lens into where these models shine, where they fall short, and where new research might go next.
Keywords
Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Deep Learning Architectures, Image Processing, Model Comparison, Artificial Intelligence Applications.
References
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How to cite this paper
@article{1708531,
author = {Rakesh Jindal, Amisha Naik, K L Ganatre, Rajat Gupta},
title = {Comprehensive Analysis and Comparison of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs)},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {6},
pages = {415-421},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708531.pdf},
abstract = {CNNs and GANs, whether working solo or combined, have sparked major shifts in AI, each playing distinct roles in the deep learning landscape. This write-up dives into a fairly deep dive and comparison of both models, digging into their internal designs, pros and cons, and practical uses. CNNs, famous for their knack for pulling out visual features, have pushed forward image tasks like classification and detection, along with medical imaging. Meanwhile, GANs shook up the generative scene by producing incredibly lifelike visuals and data. Along the way, this review uncovers key differences in how CNNs and GANs are trained, how complex GANs can get, and the ways their performance gets measured?while also pointing out common hurdles like CNN overfitting or GAN instability. On top of that, it looks into how mixing the two can lead to hybrid systems, useful in areas like synthetic data generation or style transfer. The aim here is to give researchers and engineers a helpful lens into where these models shine, where they fall short, and where new research might go next.},
keywords = {Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Deep Learning Architectures, Image Processing, Model Comparison, Artificial Intelligence Applications.},
month = {December},
}